diff --git a/EVALUATION_PROTOCOL.md b/EVALUATION_PROTOCOL.md
index d1a207bfced4648fb2d64a932554d171cfb038e0..e5e465fe1aa886aeafae3252f1d6aeb37d803833 100644
--- a/EVALUATION_PROTOCOL.md
+++ b/EVALUATION_PROTOCOL.md
@@ -70,25 +70,25 @@ are not foundation models.
## Current Limitations
-- Cross-episode generalization is evaluated in the later multi-episode stage.
+- Cross-episode generalization for Qwen3-Omni has a first verified diagnostic pilot, but strong model quality is not yet shown.
- Feature-vector reconstruction is separate from pixel depth, mesh, NeRF, or Gaussian reconstruction.
-- Qwen3-Omni setup artifacts are preparation artifacts until the selected held-out pilot runs.
+- The verified validation-aware Qwen3-Omni diagnostic pilot has weak held-out metrics and needs structured-output and task-quality improvements before larger model-quality claims.
- Full audio-visual representation learning still needs multi-episode training; the current report includes single-episode audio/no-audio ablations.
## Scale-Up Gate
-The full Qwen3-Omni fine-tuning pilot requires all of the following before
-reporting held-out model metrics:
+The next Qwen3-Omni quality pilot requires all of the following before
+claiming improved held-out model quality:
- selected prepared Xperience-10M episodes
- held-out episode split with no train/test episode leakage
+- validation samples during training
- manifest, training metadata, progress logs, metrics, predictions, and run report
- held-out evaluation on test episodes rather than train windows
-Current status: prepared; selected data relay in progress. Read
-`results/omni_finetune/DATA_ACCESS_STATUS.md` and
-`results/omni_finetune/MULTI_EPISODE_ACCESS_STATUS.md` before interpreting any
-Qwen3-Omni artifact.
+Current status: verified diagnostic pilot; quality target not met. Read
+`docs/data/omni_finetune_verified_result.json` before interpreting any
+Qwen3-Omni metric.
## Machine-Readable Copy
diff --git a/PROJECT_STATUS.md b/PROJECT_STATUS.md
index c472ce930328890b8b8e3276ebaa94f9587b70f5..0de42c9877165501fee243385fd808ea7ebadae9 100644
--- a/PROJECT_STATUS.md
+++ b/PROJECT_STATUS.md
@@ -2,8 +2,8 @@
This is the fastest way to understand the current research project state.
It summarizes what has already been implemented from the public
-Xperience-10M sample, what is being prepared for multi-episode training, and
-which artifacts support the next development step.
+Xperience-10M sample, what the first multi-episode Qwen3-Omni diagnostic pilot
+shows, and which artifacts support the next development step.
## Research Positioning
@@ -21,15 +21,15 @@ scale-up readiness; it is not presented as final full-dataset model quality.
| Neural heads | Verified | `scripts/neural_task_models.py`, `results/episode_task_suite/neural_mlp/` | Each task also has a compact PyTorch MLP run over the same feature tensor and chronological split. |
| Audio contribution study | Verified | `scripts/audio_ablation_and_raw_upgrade.py`, `results/audio_ablation/`, `docs/data/audio_ablation_summary.json` | Audio variants are compared across all 12 task contracts; audio improves the primary metric on 6 of 12 tasks, and a 588-d audio-window representation improves over the baseline audio variant on 6 of 12 tasks. |
| Research takeaways | Verified | `RESEARCH_TAKEAWAYS.md`, `docs/data/research_takeaways.json`, `scripts/build_research_takeaways.py` | The main result interpretation is generated from committed metrics: chronological class shift, neural gains on dynamics/order/alignment, open retrieval/reconstruction problems, and the need for held-out episodes. |
-| Research roadmap | Current | `RESEARCH_ROADMAP.md`, `docs/data/research_roadmap.json` | The roadmap connects public-sample task development to 128-episode data preparation, Qwen3-Omni LoRA, foundation-model selection, robustness runs, world/policy branches, and the future Xperience-native pretraining goal. |
+| Research roadmap | Current | `RESEARCH_ROADMAP.md`, `docs/data/research_roadmap.json` | The roadmap connects public-sample task development to the verified validation-aware Qwen3-Omni diagnostic baseline, structured-output improvement pass, robustness runs, world/policy branches, and the future Xperience-native pretraining goal. |
| Foundation-model plan | Current | `FOUNDATION_MODEL_PLAN.md`, `docs/data/foundation_model_plan.json` | Qwen3-Omni remains the first trainable held-out LoRA baseline; Cosmos 3 is added as the first world-model/action-generation branch; OpenVLA/openpi/GR00T are policy candidates after action targets are explicit. |
| Xperience Embodied Foundation Model | Future goal | `XPERIENCE_EMBODIED_FOUNDATION_MODEL_PRETRAINING.md` | A future full-corpus pretraining plan describes target modules, objectives, staged scale-up, hardware ranges, and evaluation for a domain-specific embodied foundation model. |
| Evaluation protocol | Verified | `EVALUATION_PROTOCOL.md`, `docs/data/evaluation_protocol.json`, `scripts/build_evaluation_protocol.py` | Windowing, chronological split, per-task metrics, leakage controls, and current limitations are generated from committed metric artifacts. |
| Dataset context | Verified | `XPERIENCE10M_DATASET_CARD_ALIGNMENT.md`, official Xperience-10M and sample cards | The README and dashboard distinguish the public sample used here from the gated full dataset used for the selected multi-episode pilot. |
-| Public dashboard and Hub pages | Verified | GitHub Pages, HF Space, artifact dataset, baseline model repo, Qwen3-Omni LoRA repo | Readers can move between the website, code, derived artifacts, baseline weights, and Qwen3-Omni pilot status without needing internal setup details. |
+| Public dashboard and Hub pages | Verified | GitHub Pages, HF Space, artifact dataset, baseline model repo, Qwen3-Omni LoRA repo | Readers can move between the website, code, derived artifacts, baseline weights, and Qwen3-Omni pilot status without needing local infrastructure details. |
| Public package policy | Verified | `DATA_NOTICE.md`, `REPRODUCIBILITY.md` | Raw Xperience-10M data, private gated files, large archives, credentials, and full Qwen weights are not redistributed. |
| Reproducibility | Verified for the public sample | `REPRODUCIBILITY.md`, `docs/data/reproducibility_matrix.json`, `notes/reproducibility_audit.md` | The public sample workflow has explicit commands, expected outputs, and exact-match reproduction evidence. |
-| Qwen3-Omni fine-tuning | Data preparation; full metrics pending | `results/omni_finetune/DATA_ACCESS_STATUS.md`, `results/omni_finetune/MULTI_EPISODE_ACCESS_STATUS.md` | The gated full dataset is available for a selected 128-episode pilot; final held-out metrics require completed preprocessing, manifest construction, training, and evaluation. |
+| Qwen3-Omni fine-tuning | Verified validation-aware diagnostic held-out pilot; quality target not met | `docs/data/omni_finetune_verified_result.json`, `results/omni_finetune/verified_public/`, `scripts/omni/package_verified_omni_result.py`, `scripts/omni/audit_verified_omni_package.py` | The selected 96/16/16 episode split produced a validation-aware public-safe held-out package with 3,808 exported windows, 512 validation windows, and 448 test predictions. JSON validity is 87.50%, below the 98% target, so the result is a diagnostic baseline and the next pass should focus on structured-output improvements and error analysis. |
| Raw Xperience-10M redistribution | Not included | `DATA_NOTICE.md`, `docs/data/publication_audit.json` | Raw MP4, HDF5, RRD files, private gated data, and full Qwen weights are intentionally excluded. |
## Fast Research Route
@@ -53,15 +53,18 @@ scale-up readiness; it is not presented as final full-dataset model quality.
controls.
10. Inspect `XPERIENCE10M_DATASET_CARD_ALIGNMENT.md` only if you need the
detailed upstream dataset-card context.
-11. Inspect `results/omni_finetune/DATA_ACCESS_STATUS.md` before judging
- Qwen3-Omni scale-up status.
+11. Inspect `docs/data/omni_finetune_verified_result.json` before judging the
+ Qwen3-Omni diagnostic pilot.
## Current Reading Notes
- Cross-episode generalization is a later multi-episode evaluation target; the
current results use one public sample episode.
-- Older pilot path names refer to setup files, not completed held-out
- training results.
+- Public-facing fine-tuning results should come from the verified result
+ package, not from live process logs or setup-only artifacts.
+- The first Qwen3-Omni held-out package verifies the pipeline, not strong model
+ quality: JSON validity is 87.50%, action macro-F1 is 0.0027, and subtask
+ accuracy is 0.0067.
- The current reconstruction task reconstructs feature vectors, not pixel
depth, meshes, NeRF outputs, or Gaussian splats.
- Audio is part of the current 8,546-dimensional baseline feature vector.
diff --git a/README.md b/README.md
index a3e111243371d495a814ceeba3d3e31a1e80b732..88e8e6b3b8a98ed883a41a18e96499555ab27a2e 100644
--- a/README.md
+++ b/README.md
@@ -1,110 +1,1124 @@
----
-license: other
-pretty_name: Ropedia Xperience-10M Task Suite Artifacts
-tags:
- - robotics
- - embodied-ai
- - multimodal
- - ropedia
- - xperience-10m
- - evaluation
- - baseline
- - neural-network
- - pytorch
- - retrieval
- - audio
-task_categories:
- - robotics
- - time-series-forecasting
- - text-retrieval
-language:
- - en
-size_categories:
- - 1K
+
+
-- 1 public Xperience-10M sample episode
-- 5,821 frames and 1,161 aligned 20-frame windows
-- 8,546-dimensional multimodal task representation
-- 12 minimal task heads, 12 compact neural MLP heads, and 4 extension probes
-- single-episode chronological split; selected multi-episode pilot preparation is underway
-- the gated full dataset is available for a selected 128-episode pilot before held-out evaluation
-- future Xperience Embodied Foundation Model pretraining is documented as a long-term full-corpus goal, not as a completed model
+A research-development project built on the public Xperience-10M sample episode
+released by Ropedia. The goal is to make one richly multimodal egocentric
+episode understandable, turn it into concrete embodied-AI task definitions, and
+prepare the same pipeline for future held-out multi-episode training.
-
+## What To Open First
-
+Start with `PROJECT_README.md` for the full project walkthrough, `PROJECT_STATUS.md`
+for the current evidence boundary, and `docs/index.html` for the static dashboard
+copy. The current Qwen3-Omni validation-aware diagnostic result is summarized in
+`docs/data/omni_finetune_verified_result.json`.
-
+## Dataset Boundary
-The LoRA pipeline figure shows how prepared valid Xperience-10M episodes become
-split-safe Qwen3-Omni training records, adapter inputs, predictions, metrics,
-run reports, and upload-ready LoRA artifacts. It documents the training path;
-final held-out model-quality metrics remain pending completed multi-episode
-training and evaluation.
+This artifact dataset contains derived windows, manifests, metrics, predictions,
+figures, reports, and public-safe Qwen3-Omni diagnostic summaries. Raw Xperience-10M videos,
+HDF5 annotations, RRD visualizations, private gated data, Qwen base weights, and
+LoRA adapter weights are not redistributed here.
-## What To Open First
+## Related Hub Repositories
+
+- Space: https://huggingface.co/spaces/cy0307/ropedia-xperience-10m-task-suite
+- Baseline model repo: https://huggingface.co/cy0307/ropedia-xperience-10m-task-baselines
+- Qwen3-Omni diagnostic LoRA repo: https://huggingface.co/cy0307/ropedia-qwen3-omni-lora-smoke
+- Official gated dataset: https://huggingface.co/datasets/ropedia-ai/xperience-10m
+- Public sample dataset: https://huggingface.co/datasets/ropedia-ai/xperience-10m-sample
+
+The central research questions are:
+
+- What can be learned from one aligned Xperience-10M episode while separating
+ sample-specific observations from later multi-episode questions?
+- Which input/output tasks are meaningful for embodied AI when video, depth,
+ pose, mocap, IMU, and language annotations are synchronized?
+- What baseline models and evaluation files should exist before scaling to
+ Qwen3-Omni or other multimodal foundation-model fine-tuning?
-| Reader goal | Start here |
+## Why This Project Exists
+
+This project is organized as a compact research artifact around Xperience-10M:
+start from a real public episode, make every modality and label path inspectable,
+turn the data into concrete embodied-AI tasks, and keep the evaluation boundary
+clear while preparing the next multi-episode experiments. The emphasis is on
+research judgment as much as implementation: what the sample can show, what it
+cannot show, and what evidence should exist before claiming model quality.
+
+The work is designed to demonstrate four capabilities that matter for
+embodied-AI research infrastructure:
+
+| Capability | What this project shows |
| --- | --- |
-| Understand the whole project | `PROJECT_BRIEF.md`, `PROJECT_STATUS.md`, `PROJECT_README.md` |
-| Inspect the 12 task results | `results/episode_task_suite/summary_report.json`, `RESEARCH_TAKEAWAYS.md` |
-| Compare baseline model outputs | `results/episode_task_suite/`, `results/episode_task_suite/neural_mlp/` |
-| Explore one episode visually | `docs/single_episode_explorer.html`, `docs/research_roadmap.html` |
-| Understand audio's contribution | `results/audio_ablation/AUDIO_ABLATION_SUMMARY.md` |
-| Follow the Qwen3-Omni scale-up path | `FOUNDATION_MODEL_PLAN.md`, `results/omni_finetune/DATA_ACCESS_STATUS.md` |
-| Explore additional development directions | `ADDITIONAL_DEVELOPMENT_DIRECTIONS.md`, `docs/data/additional_development_directions.json` |
-| Read the future native pretraining goal | `XPERIENCE_EMBODIED_FOUNDATION_MODEL_PRETRAINING.md` |
+| Multimodal data understanding | Parses the public sample into synchronized windows across video, audio, depth, pose/SLAM, mocap, IMU, calibration, and language-derived signals |
+| Task design | Defines 12 human-readable tasks plus four direction-extension probes with inputs, outputs, process modules, metrics, and case-study walkthroughs |
+| Model and evaluation discipline | Runs minimal and compact neural baselines, records predictions/metrics, keeps chronological split boundaries explicit, and separates sample evidence from held-out claims |
+| Scale-up planning | Connects the public-sample pipeline to 32/128-episode held-out pilots, Qwen3-Omni LoRA, Cosmos-style world-model branches, policy-model branches, and the future Xperience-native foundation-model pretraining goal |
-## Related Hub Repositories
+## Start Here
-| Repository | Role |
+For a first pass, use [`PROJECT_BRIEF.md`](PROJECT_BRIEF.md) or the
+machine-readable [`docs/data/project_brief.json`](docs/data/project_brief.json).
+They give the project shape in one page: what exists now, what the public
+sample can support, where the 12 tasks and baselines live, and what must happen
+before the multi-episode omni-model stage becomes a real held-out evaluation.
+
+| Reader goal | Best entry point |
| --- | --- |
-| [`cy0307/ropedia-xperience-10m-task-baselines`](https://huggingface.co/cy0307/ropedia-xperience-10m-task-baselines) | Minimal and neural baseline task-head weights for these artifacts |
-| [`cy0307/ropedia-qwen3-omni-lora-smoke`](https://huggingface.co/cy0307/ropedia-qwen3-omni-lora-smoke) | Qwen3-Omni PEFT LoRA smoke adapter from the initial end-to-end readiness run |
-| [`cy0307/ropedia-xperience-10m-task-suite`](https://huggingface.co/spaces/cy0307/ropedia-xperience-10m-task-suite) | Interactive project dashboard |
-| [`ghcr.io/chaoyue0307/ropedia-xperience-10m-task-suite`](https://github.com/ChaoYue0307/ropedia-xperience-10m-task-suite/pkgs/container/ropedia-xperience-10m-task-suite) | GitHub Container Registry package for the static dashboard |
-| [`ropedia-ai/xperience-10m`](https://huggingface.co/datasets/ropedia-ai/xperience-10m) | Official gated upstream Xperience-10M dataset |
-| [`ropedia-ai/xperience-10m-sample`](https://huggingface.co/datasets/ropedia-ai/xperience-10m-sample) | Public sample episode source |
+| Understand the whole project quickly | [`PROJECT_BRIEF.md`](PROJECT_BRIEF.md) |
+| See the visual research dashboard | [GitHub Pages dashboard](https://chaoyue0307.github.io/ropedia-xperience-10m-task-suite/) |
+| Navigate the 12 tasks, four tracks, and scale-up plan | [Interactive research roadmap](https://chaoyue0307.github.io/ropedia-xperience-10m-task-suite/research_roadmap.html), [`docs/data/research_roadmap_interactive.json`](docs/data/research_roadmap_interactive.json) |
+| Compare current task metrics | [`RESEARCH_TAKEAWAYS.md`](RESEARCH_TAKEAWAYS.md), [`docs/data/summary_metrics.json`](docs/data/summary_metrics.json) |
+| Compare possible foundation backbones | [`FOUNDATION_MODEL_PLAN.md`](FOUNDATION_MODEL_PLAN.md), [`docs/data/foundation_model_plan.json`](docs/data/foundation_model_plan.json) |
+| Understand the future native pretraining goal | [`XPERIENCE_EMBODIED_FOUNDATION_MODEL_PRETRAINING.md`](XPERIENCE_EMBODIED_FOUNDATION_MODEL_PRETRAINING.md) |
+| See additional concrete project directions | [`ADDITIONAL_DEVELOPMENT_DIRECTIONS.md`](ADDITIONAL_DEVELOPMENT_DIRECTIONS.md), [`docs/data/additional_development_directions.json`](docs/data/additional_development_directions.json) |
+| Understand one model input | [`results/episode_task_suite/feature_manifest.json`](results/episode_task_suite/feature_manifest.json), [`results/episode_task_suite/windows.csv`](results/episode_task_suite/windows.csv) |
+| Check multi-episode data status | [`results/omni_finetune/DATA_ACCESS_STATUS.md`](results/omni_finetune/DATA_ACCESS_STATUS.md) |
-## Dataset Boundary
+## Research Project Overview
+
+| Theme | Current implementation |
+| --- | --- |
+| Dataset slice | One public Xperience-10M sample episode, 5,821 frames, 1,161 windows, and an 8,546-dimensional representation |
+| Modalities | Video, audio, depth, camera pose/SLAM, hand/body mocap, IMU, calibration, and language annotations |
+| Task suite | 12 human-readable embodied-AI task contracts with input, process, output, metrics, predictions, and case-study walkthroughs |
+| Baselines | Minimal linear/ridge/logistic heads plus compact PyTorch MLP task heads over the same chronological split |
+| Research directions | Task mapping and extension probes for human modeling, 3D/4D reconstruction, egocentric interaction, and world modeling |
+| Scale-up path | A first selected-episode Qwen3-Omni LoRA diagnostic pilot has completed on the 96/16/16 split; it proves the multi-episode export/train/eval/package loop, but the weak held-out metrics make it a baseline for error analysis rather than a strong model. Cosmos 3/world-model and VLA/policy branches reuse the same split and package contract after their targets are implemented. |
+| Public surfaces | GitHub repo, GitHub Pages dashboard, GHCR static-site package, HF Space, HF artifact dataset, HF baseline-model repo, and HF collection |
+
+For the fastest interpretation of the current metrics, start with
+[`RESEARCH_TAKEAWAYS.md`](RESEARCH_TAKEAWAYS.md) and
+[`docs/data/research_takeaways.json`](docs/data/research_takeaways.json).
+They summarize what the public sample results actually show: class shift under
+chronological splits, neural gains on dynamics/order/alignment, harder
+retrieval/reconstruction probes, and why the next model-quality step needs
+held-out episodes.
+
+Current contributions:
+
+- manifested sliding-window features over the currently extracted modalities,
+- motion-only and current all-feature baseline models,
+- 12 end-to-end episode-level tasks,
+- lightweight neural MLP heads for the same 12 task contracts,
+- a generated four-direction research taxonomy matching the Ropedia job tracks,
+- four additional direction-extension probes with minimal and neural baselines,
+- human-readable research task cards and an interactive scrub/play walkthrough storyboard for every task,
+- an interactive research roadmap connecting 12 tasks, four research tracks, current sample evidence, the Qwen3-Omni scale-up path, and foundation-model branch selection,
+- a next-milestone track for Qwen3-Omni fine-tuning, Cosmos 3 world modeling, and sensor-bridge evaluation,
+- a future pretraining plan for an Xperience Embodied Foundation Model over the full corpus after smaller multi-episode stages prove value,
+- metrics, predictions, model weights, manifests, charts, and a two-level
+ tabbed static research website,
+- a clear explanation of what is implemented now and what moves to the multi-episode stage.
+
+## Current Research Scope
+
+This project is best read as a staged embodied-AI research study:
+
+| Layer | Current scope | Where to start |
+| --- | --- | --- |
+| Data understanding | One public Xperience-10M sample episode is converted into 5,821 frames, 1,161 aligned windows, and an 8,546-dimensional multimodal representation. | [`PROJECT_BRIEF.md`](PROJECT_BRIEF.md), [`PROJECT_STATUS.md`](PROJECT_STATUS.md) |
+| Task suite | Twelve human-readable tasks cover action, procedure, contact, object, language, retrieval, reconstruction, order, and synchronization questions. | [`RESEARCH_TAKEAWAYS.md`](RESEARCH_TAKEAWAYS.md), [`results/episode_task_suite/summary_report.json`](results/episode_task_suite/summary_report.json) |
+| Baselines | Minimal heads and compact PyTorch MLP heads provide a first controlled comparison on the same chronological split. | [`results/episode_task_suite/neural_mlp/`](results/episode_task_suite/neural_mlp/) |
+| Diagnostics | Audio contribution, modality ablations, timeline overlays, object labels, and alignment stress tests show which signals are useful and which tasks remain hard. | [`results/audio_ablation/AUDIO_ABLATION_SUMMARY.md`](results/audio_ablation/AUDIO_ABLATION_SUMMARY.md), [`docs/single_episode_explorer.html`](docs/single_episode_explorer.html) |
+| Scale-up | The selected 128-episode Qwen3-Omni LoRA diagnostic pilot has a verified validation-aware held-out package: 96/16/16 selected episodes, 3,808 exported windows, 512 validation windows, 448 held-out test windows, and public-safe metrics/predictions. JSON validity is 87.50%, below the 98% target, so the next pass focuses on structured-output reliability and task-quality error analysis. | [`RESEARCH_ROADMAP.md`](RESEARCH_ROADMAP.md), [`FOUNDATION_MODEL_PLAN.md`](FOUNDATION_MODEL_PLAN.md), [`docs/data/omni_finetune_verified_result.json`](docs/data/omni_finetune_verified_result.json), [`results/omni_finetune/verified_public/`](results/omni_finetune/verified_public/) |
+
+Detailed dataset notes, reproduction checks, and generated JSON reports are
+included for readers who want to inspect the implementation, but they are
+supporting materials rather than the main reading path. Use
+[`ARTIFACT_GUIDE.md`](ARTIFACT_GUIDE.md) when you want the full file map.
+
+## Project Status
+
+If you only have one minute, use
+[`PROJECT_STATUS.md`](PROJECT_STATUS.md) and
+[`docs/data/project_status.json`](docs/data/project_status.json).
+They give the current research state in one compact table:
+
+| Area | Current decision |
+| --- | --- |
+| Public-sample pipeline | Verified on one public sample episode: 5,821 frames, 1,161 windows, 8,546 dimensions |
+| 12-task suite | Verified minimal baselines with committed metrics, predictions, and manifests |
+| Neural heads | Verified compact PyTorch MLP heads over the same task contracts and chronological splits |
+| Dataset context | Official Xperience-10M links, sample-vs-gated-data boundary, modality coverage, and redistribution policy are documented |
+| Evaluation protocol | Verified generated protocol for windowing, split policy, leakage controls, and per-task metrics |
+| Website and Hub pages | Public dashboard, Hugging Face Space, artifact dataset, baseline model repo, and collection use the same project framing and links |
+| Qwen3-Omni multi-episode pilot | Verified diagnostic result package exists for the selected 96/16/16 episode split; current held-out metrics are weak and below the JSON-validity quality target |
+| Raw Xperience-10M data / full Qwen weights | Not redistributed |
+
+## 90-Second Research Project Path
+
+If you are reading the project cold, open these in order:
+
+| Step | Question | Primary artifacts | What should be true |
+| --- | --- | --- | --- |
+| 1 | What is this project? | [`PROJECT_BRIEF.md`](PROJECT_BRIEF.md), [`PROJECT_STATUS.md`](PROJECT_STATUS.md), [dashboard](https://chaoyue0307.github.io/ropedia-xperience-10m-task-suite/) | A public-sample Xperience-10M research project with 12 tasks, baselines, and a scale-up plan. |
+| 2 | What data is used? | [`XPERIENCE10M_DATASET_CARD_ALIGNMENT.md`](XPERIENCE10M_DATASET_CARD_ALIGNMENT.md), [official HF dataset](https://huggingface.co/datasets/ropedia-ai/xperience-10m), [sample HF dataset](https://huggingface.co/datasets/ropedia-ai/xperience-10m-sample) | The implemented suite uses one public sample episode; the gated dataset is reserved for selected multi-episode training. |
+| 3 | What does one model input contain? | [`windows.csv`](results/episode_task_suite/windows.csv), [`feature_manifest.json`](results/episode_task_suite/feature_manifest.json), [`available_modalities.json`](results/episode_task_suite/available_modalities.json) | Each window is an aligned multimodal unit with video, audio, depth, pose/SLAM, mocap, IMU, calibration, and language-derived signals. |
+| 4 | What are the 12 tasks? | [`results/episode_task_suite/task_walkthroughs/`](results/episode_task_suite/task_walkthroughs/), [`docs/data/task_walkthroughs.json`](docs/data/task_walkthroughs.json) | Every task has a human-readable name, case study, input, process modules, output, metric, and limitation. |
+| 5 | How are tasks evaluated? | [`EVALUATION_PROTOCOL.md`](EVALUATION_PROTOCOL.md), [`docs/data/evaluation_protocol.json`](docs/data/evaluation_protocol.json) | The window unit, chronological split, leakage controls, task metrics, and current limitations are explicit. |
+| 6 | What do the current results mean? | [`RESEARCH_TAKEAWAYS.md`](RESEARCH_TAKEAWAYS.md), [`docs/data/research_takeaways.json`](docs/data/research_takeaways.json), [`docs/data/summary_metrics.json`](docs/data/summary_metrics.json) | Current metrics describe sample-level task behavior and identify which signals need larger held-out experiments. |
+| 7 | Which models are implemented? | [`results/episode_task_suite/summary_report.json`](results/episode_task_suite/summary_report.json), [`results/episode_task_suite/neural_mlp/`](results/episode_task_suite/neural_mlp/), [HF baseline repo](https://huggingface.co/cy0307/ropedia-xperience-10m-task-baselines) | Each task has minimal and neural-head evidence over the same feature windows. |
+| 8 | What research directions does this support? | [`RESEARCH_ROADMAP.md`](RESEARCH_ROADMAP.md), [`docs/data/research_directions.json`](docs/data/research_directions.json), [`docs/data/research_direction_extensions.json`](docs/data/research_direction_extensions.json) | The tasks are mapped to human modeling, 3D/4D reconstruction, egocentric interaction, and world modeling. |
+| 9 | Which foundation model comes next? | [`FOUNDATION_MODEL_PLAN.md`](FOUNDATION_MODEL_PLAN.md), [`docs/data/foundation_model_plan.json`](docs/data/foundation_model_plan.json), [`XPERIENCE_EMBODIED_FOUNDATION_MODEL_PRETRAINING.md`](XPERIENCE_EMBODIED_FOUNDATION_MODEL_PRETRAINING.md) | Qwen3-Omni is the first held-out LoRA baseline; Cosmos 3 is the first world-model branch; policy models wait for explicit action targets; Xperience-native pretraining is the full-corpus future goal. |
+| 10 | How do I reproduce it? | [`REPRODUCIBILITY.md`](REPRODUCIBILITY.md), [`notes/reproducibility_audit.md`](notes/reproducibility_audit.md) | Public commands and expected outputs are documented for the sample-episode task suite. |
+| 11 | What is still pending? | [`docs/data/omni_finetune_verified_result.json`](docs/data/omni_finetune_verified_result.json), [`DATA_ACCESS_STATUS.md`](results/omni_finetune/DATA_ACCESS_STATUS.md), [`MULTI_EPISODE_ACCESS_STATUS.md`](results/omni_finetune/MULTI_EPISODE_ACCESS_STATUS.md) | The first held-out diagnostic pilot is verified; strong model quality remains pending because JSON validity is 87.50% and action/subtask metrics remain weak. |
+
+A compact reader-path summary is available at
+[`docs/data/project_packet.json`](docs/data/project_packet.json).
+
+## Supporting Files
+
+[`ARTIFACT_GUIDE.md`](ARTIFACT_GUIDE.md) is the human-readable map for readers
+who want to inspect the project files after the first pass. It groups the main
+briefs, task outputs, baseline results, visual assets, data notes, and
+scale-up documents.
+
+[`docs/data/artifact_index.json`](docs/data/artifact_index.json) is the compact
+machine-readable companion used by the website and Hugging Face artifact
+dataset.
+
+## Evaluation Protocol
+
+[`EVALUATION_PROTOCOL.md`](EVALUATION_PROTOCOL.md) and
+[`docs/data/evaluation_protocol.json`](docs/data/evaluation_protocol.json) are
+generated from committed metric artifacts. They define:
+
+- the 20-frame window unit, stride, feature dimension, and raw-data policy,
+- the chronological 70/30 single-episode split and its generalization limit,
+- the per-task input, target, primary metric, minimal score, and neural score,
+- leakage controls for future labels, target-side signals, caption/object
+ labels, and train-only normalization,
+- current limitations, including cross-episode generalization,
+ audio-visual learning, pixel-depth reconstruction, and real held-out
+ multi-episode Qwen3-Omni quality.
+
+## Dataset Context
+
+The official [`ropedia-ai/xperience-10m`](https://huggingface.co/datasets/ropedia-ai/xperience-10m)
+dataset is a gated large-scale egocentric multimodal dataset for embodied AI,
+robotics, spatial intelligence, and world modeling. The public
+[`ropedia-ai/xperience-10m-sample`](https://huggingface.co/datasets/ropedia-ai/xperience-10m-sample)
+repo provides the sample episode used for the implemented task suite here.
+
+This project keeps those layers separate: the public sample supports the
+current 12-task study, while the gated full dataset is used only for the
+selected multi-episode Qwen3-Omni pilot. Raw Xperience-10M MP4/HDF5/RRD files
+are not redistributed in this repo or in the Hugging Face mirrors.
+
+The current verified public-sample subset is:
+
+- one public sample episode, 5,821 frames, and 1,161 aligned windows,
+- raw sample files with six MP4 video streams and audio streams,
+- `annotation.hdf5` carrying depth, SLAM/camera pose, hand/body mocap, IMU,
+ language/caption annotations, calibration, metadata, and timing records,
+- an 8,546-dimensional baseline representation using video, audio, depth,
+ pose/SLAM, mocap, IMU, calibration, and language-derived signals.
+
+Detailed dataset notes are available in
+[`XPERIENCE10M_DATASET_CARD_ALIGNMENT.md`](XPERIENCE10M_DATASET_CARD_ALIGNMENT.md)
+for readers who need the full upstream-card and access-term context. The
+practical boundary is simple: current task-suite results come from the public
+sample, and the first multi-episode Qwen3-Omni diagnostic pilot is verified but
+not yet strong model quality.
+
+Start with the visual dashboard:
+
+**[chaoyue0307.github.io/ropedia-xperience-10m-task-suite](https://chaoyue0307.github.io/ropedia-xperience-10m-task-suite/)**
+
+Hugging Face Space app:
+
+**[cy0307-ropedia-xperience-10m-task-suite.static.hf.space](https://cy0307-ropedia-xperience-10m-task-suite.static.hf.space/)**
+
+## Read This Project In Three Layers
+
+| Layer | What to inspect | Why it matters |
+| --- | --- | --- |
+| Project status | `PROJECT_STATUS.md`, `docs/data/project_status.json` | Gives a one-table current project summary before reading the full artifact trail |
+| Data contract | `windows.csv`, `feature_manifest.json`, modality manifests | Confirms what each sample window contains before modeling |
+| Dataset context | `XPERIENCE10M_DATASET_CARD_ALIGNMENT.md`, official dataset links | Explains the official dataset, public sample, modalities, access boundary, and what this repo uses |
+| Visual assets | `FIGURE_INDEX.md`, `docs/assets/` | Shows the task-suite graphic, modality thumbnails, pipeline diagrams, charts, and logo assets |
+| Evaluation protocol | `EVALUATION_PROTOCOL.md`, `docs/data/evaluation_protocol.json` | Defines the task unit, split, metrics, leakage controls, and current limitations |
+| Research roadmap | `RESEARCH_ROADMAP.md`, `docs/data/research_roadmap.json` | Shows the path from sample-level task development to multi-episode work, larger model branches, and the future native-pretraining goal |
+| Additional development directions | `ADDITIONAL_DEVELOPMENT_DIRECTIONS.md`, `docs/data/additional_development_directions.json` | Records concrete non-backbone tracks: taxonomy, benchmark protocol, representation learning, skill graphs, affordances, 3D/4D memory, QA, and policy transfer |
+| Xperience Embodied Foundation Model plan | `XPERIENCE_EMBODIED_FOUNDATION_MODEL_PRETRAINING.md` | Describes the long-term full-corpus pretraining goal, target modules, objectives, staged scale-up, hardware ranges, and evaluation protocol |
+| Minimal heads | softmax, ridge projection/regression, multi-label logistic heads | Keeps every input/output contract visible and inspectable |
+| Neural heads | PyTorch MLP classifiers/regressors under `neural_mlp/` | Checks whether nonlinear heads improve each task without changing features |
+| Evidence | metrics, predictions, confusion matrices, diagrams, dashboard | Makes the single-episode task development inspectable without rerunning first |
+| Artifact guide | `ARTIFACT_GUIDE.md` | Groups the public evidence into research-project layers after the first-pass overview |
+| Reproducibility contract | `REPRODUCIBILITY.md`, `docs/data/reproducibility_matrix.json` | States public commands, expected outputs, exact-match reproduction evidence, and non-reproducible boundaries |
+| Citation metadata | `CITATION.cff`, `codemeta.json`, `LICENSE` | Makes the repo easier to cite, index, and reuse without confusing code license and dataset terms |
+
+## Links
+
+| Resource | Link |
+| --- | --- |
+| This GitHub repo | [github.com/ChaoYue0307/ropedia-xperience-10m-task-suite](https://github.com/ChaoYue0307/ropedia-xperience-10m-task-suite) |
+| This project website | [chaoyue0307.github.io/ropedia-xperience-10m-task-suite](https://chaoyue0307.github.io/ropedia-xperience-10m-task-suite/) |
+| This Hugging Face Space | [huggingface.co/spaces/cy0307/ropedia-xperience-10m-task-suite](https://huggingface.co/spaces/cy0307/ropedia-xperience-10m-task-suite) |
+| Live Hugging Face static app | [cy0307-ropedia-xperience-10m-task-suite.static.hf.space](https://cy0307-ropedia-xperience-10m-task-suite.static.hf.space/) |
+| GitHub Container package | [ghcr.io/chaoyue0307/ropedia-xperience-10m-task-suite](https://github.com/ChaoYue0307/ropedia-xperience-10m-task-suite/pkgs/container/ropedia-xperience-10m-task-suite) |
+| Derived artifacts on Hugging Face | [huggingface.co/datasets/cy0307/ropedia-xperience-10m-task-suite-artifacts](https://huggingface.co/datasets/cy0307/ropedia-xperience-10m-task-suite-artifacts) |
+| Minimal and neural task baselines on Hugging Face | [huggingface.co/cy0307/ropedia-xperience-10m-task-baselines](https://huggingface.co/cy0307/ropedia-xperience-10m-task-baselines) |
+| Hugging Face collection | [huggingface.co/collections/cy0307/ropedia-xperience-10m-task-suite](https://huggingface.co/collections/cy0307/ropedia-xperience-10m-task-suite) |
+| Xperience-10M dataset website | [ropedia.com/dataset](https://ropedia.com/dataset) |
+| Xperience-10M release page | [ropedia.com/blog/20260316_xperience_10m](https://ropedia.com/blog/20260316_xperience_10m) |
+| Ropedia GitHub organization | [github.com/Ropedia](https://github.com/Ropedia) |
+| HOMIE Toolkit | [github.com/Ropedia/HOMIE-toolkit](https://github.com/Ropedia/HOMIE-toolkit) |
+| Xperience-10M Hugging Face dataset | [huggingface.co/datasets/ropedia-ai/xperience-10m](https://huggingface.co/datasets/ropedia-ai/xperience-10m) |
+| Xperience-10M sample on Hugging Face | [huggingface.co/datasets/ropedia-ai/xperience-10m-sample](https://huggingface.co/datasets/ropedia-ai/xperience-10m-sample) |
+| Ropedia Hugging Face organization | [huggingface.co/ropedia-ai](https://huggingface.co/ropedia-ai) |
+
+## Citation, License, And Metadata
+
+Use [`CITATION.cff`](CITATION.cff) when citing this project. The repository
+also includes [`codemeta.json`](codemeta.json) for machine-readable software
+metadata and [`docs/data/project_manifest.json`](docs/data/project_manifest.json)
+for website/Hugging Face surface metadata.
+
+The code files are MIT-licensed. Raw Xperience-10M data is not redistributed
+here, and dataset use remains governed by the official Ropedia/Xperience-10M
+terms. See [`LICENSE`](LICENSE) and [`DATA_NOTICE.md`](DATA_NOTICE.md).
+
+
+
+The infographic uses a custom text-free research background and puts the shared
+processing contract plus all 12 task families before the modality atlas.
+Public-sample modality thumbnails remain enlarged below the task map. The task
+names, input/output summaries, and metrics are overlaid from
+[`results/episode_task_suite/summary_report.json`](results/episode_task_suite/summary_report.json)
+with [`scripts/render_task_suite_infographic.py`](scripts/render_task_suite_infographic.py),
+so the published PNG is a presentation graphic with verified labels and metrics,
+not a hallucinated metric sheet.
+
+The website also includes a responsive native modality atlas backed by
+[`docs/data/modality_atlas.json`](docs/data/modality_atlas.json) and
+[`docs/assets/modalities/`](docs/assets/modalities/). Those assets are small
+derived thumbnails from the public sample, not raw Xperience-10M files.
+
+
+
+
+
+
+
+The pipeline and architecture figures use the same pattern: text-free visual
+backgrounds carry the composition, while
+[`scripts/render_overview_figures.py`](scripts/render_overview_figures.py)
+overlays exact labels, dimensions, and metrics from the committed result files.
+
+## Scope
+
+This is a learning, inspection, and pipeline-validation repo built from one
+public sample episode. The next model-quality stage is to run the same suite
+over many episodes and split train/test by held-out episode.
+
+## What Is Inside
+
+```text
+scripts/
+ train_min_action_model.py # motion/IMU baseline
+ train_all_modalities_model.py # current all-feature lightweight baseline
+ episode_task_suite.py # 12 end-to-end task definitions
+ neural_task_models.py # optional PyTorch MLP heads for all 12 tasks
+ research_direction_taxonomy.py # maps 12 tasks to the four research tracks
+ research_direction_extension_tasks.py # one extra data-backed probe per track
+ task_walkthroughs.py # human-readable task-card and walkthrough-storyboard metadata
+ generate_visualizations.py # refreshes SVG charts + summary JSON
+ render_task_suite_infographic.py # renders the task-suite presentation PNG
+ export_modality_atlas_assets.py # exports responsive modality-card assets
+ render_overview_figures.py # renders polished pipeline/architecture PNGs
+ build_brand_assets.py # derives logo sizes, favicon, social card
+ build_artifact_index.py # builds the compact artifact guide data
+ build_quality_gates.py # builds release checks
+ validate_mirror_parity.py # checks prepared GitHub/HF mirror file parity
+ validate_scope_claims.py # separates setup artifacts from completed model metrics
+ validate_task_surface.py # checks readable task cards and interactive storyboard wiring
+ validate_website_integrity.py # checks local site links, anchors, and images
+ validate_publication_package.py # checks public repo + HF bundle contents
+ publish_hf_bundles.py # uploads prepared HF Space/artifact/model bundles
+ omni/
+ download_sample_modelscope.py # ModelScope sample download helper
+ build_episode_manifest.py # metadata-only multi-episode scanner
+ plan_finetune_sample_budget.py # storage/sample-count planner
+ qwen3_omni_adapter_smoke.py # real-data Qwen3-Omni adapter setup check
+
+results/
+ min_action_model/ # motion-only action baseline artifacts
+ min_subtask_model/ # motion-only subtask baseline artifacts
+ min_all_modalities_action_model/ # current all-feature action artifacts
+ min_all_modalities_subtask_model/ # current all-feature subtask artifacts
+ episode_task_suite/ # 12-task suite metrics and predictions
+ neural_mlp/ # optional neural baseline artifacts per task
+ research_directions/ # four-track taxonomy, CSV, and summary
+ research_direction_extensions/ # four extra direction probes + predictions
+ task_walkthroughs/ # case-study walkthroughs for all 12 tasks
+ omni_exploration/ # ModelScope readiness-check artifacts
+
+docs/
+ index.html # GitHub Pages dashboard
+ data/additional_development_directions.json # concrete non-backbone project directions
+ data/summary_metrics.json # website-readable metrics bundle
+ data/evidence_contract.json # machine-readable project scope
+ data/artifact_index.json # compact project-artifact catalog
+ data/live_publication_status.json # live GitHub/HF publication verification
+ data/quality_gates.json # machine-readable release checks
+ data/task_surface_integrity.json # machine-readable task-card/storyboard integrity check
+ data/project_manifest.json # machine-readable public-surface metadata
+ data/project_packet.json # compact project path and scope summary
+ data/research_roadmap.json # multi-episode and omni-model roadmap
+ data/research_directions.json # four-track website data bundle
+ data/research_direction_extensions.json # four extra probe data bundle
+ data/task_walkthroughs.json # human-readable task-card and walkthrough-storyboard data
+ data/modality_atlas.json # responsive modality-card data
+ assets/brand/*.png # project logo, favicon, social card
+ assets/task_suite_infographic.png # 12-task presentation graphic
+ assets/modalities/ # public-sample derived modality thumbnails
+ assets/pipeline_diagram.png # verified episode pipeline graphic
+ assets/qwen3_omni_lora_pipeline.png # Qwen3-Omni LoRA training-flow figure
+ assets/task_architectures.png # verified 12-task minimal architecture map
+ assets/charts/*.svg # regenerated visualizations
+
+notes/
+ min_action_model.md
+ all_modalities_model.md
+ episode_task_suite.md
+```
+
+Raw Xperience-10M data is **not** committed. Download it from the official
+Ropedia distribution and follow the dataset terms.
+
+## GitHub Package
+
+The public dashboard is packaged as a static-site container on GitHub Container
+Registry. It contains the `docs/` site plus the main reader documents; it does
+not include raw Xperience-10M videos, raw annotations, gated data, or model
+weights.
+
+```bash
+docker pull ghcr.io/chaoyue0307/ropedia-xperience-10m-task-suite:latest
+docker run --rm -p 8080:80 ghcr.io/chaoyue0307/ropedia-xperience-10m-task-suite:latest
+```
+
+Then open `http://localhost:8080`.
+
+## Data Expected
+
+The scripts expect a workspace with the Ropedia HOMIE toolkit and the
+Xperience-10M sample episode:
+
+```text
+/
+ HOMIE-toolkit/
+ data/sample/xperience-10m-sample/
+ annotation.hdf5
+ fisheye_cam0.mp4
+ fisheye_cam1.mp4
+ fisheye_cam2.mp4
+ fisheye_cam3.mp4
+ stereo_left.mp4
+ stereo_right.mp4
+```
+
+The public sample dataset identifier is:
+
+```text
+ropedia-ai/xperience-10m-sample
+```
+
+Hugging Face URL:
+
+```text
+https://huggingface.co/datasets/ropedia-ai/xperience-10m-sample
+```
+
+## Quickstart
+
+From a workspace folder:
+
+```bash
+git clone https://github.com/Ropedia/HOMIE-toolkit.git
+python3.12 -m venv .venv
+source .venv/bin/activate
+pip install -r HOMIE-toolkit/requirements.txt huggingface_hub hf_xet
+```
+
+Download the sample:
+
+```bash
+hf download ropedia-ai/xperience-10m-sample \
+ --repo-type dataset \
+ --local-dir data/sample/xperience-10m-sample
+```
+
+If Hugging Face access is unavailable in your environment, use ModelScope:
+
+```bash
+python scripts/omni/download_sample_modelscope.py \
+ --output-dir data/sample/xperience-10m-sample \
+ --mode minimal
+```
+
+`--mode minimal` downloads `annotation.hdf5`, `README.md`, and
+`fisheye_cam0.mp4`. Use `--mode all-training` to add all six MP4 streams while
+still skipping `visualization.rrd`.
+
+Clone and run this repo:
+
+```bash
+git clone https://github.com/ChaoYue0307/ropedia-xperience-10m-task-suite.git
+cd ropedia-xperience-10m-task-suite
+python scripts/episode_task_suite.py --workspace /path/to/workspace
+```
+
+Run the same 12-task suite with lightweight neural heads:
+
+```bash
+pip install torch
+python scripts/episode_task_suite.py \
+ --workspace /path/to/workspace \
+ --include-neural
+```
+
+Run the smaller baselines:
+
+```bash
+python scripts/train_min_action_model.py --workspace /path/to/workspace
+python scripts/train_all_modalities_model.py --workspace /path/to/workspace
+```
+
+## Xperience-10M Fine-Tuning Exploration
+
+This repo includes a first Qwen3-Omni fine-tuning path over Xperience-10M. The
+repository separates public-sample evidence from multi-episode fine-tuning
+artifacts. The validation-aware selected-episode held-out package is now verified as a
+diagnostic pilot, not a strong final model.
+The useful distinction is:
+
+- direct Qwen3-Omni inputs: RGB/fisheye video, embedded MP4 audio, and language
+ prompts,
+- adapter-required Xperience-10M sensor inputs: depth, pose/SLAM, hand/body
+ mocap, contacts, and IMU.
+
+
+
+The figure shows the intended end-to-end training flow: raw valid episodes enter
+episode-level split validation, parallel media/sensor export creates Qwen-style
+JSONL records, Qwen3-Omni receives video/audio/text directly, the sensor bridge
+adds depth/pose/mocap/IMU features, LoRA adapters are trained on prepared
+train/val episodes, and sealed held-out test evaluation produces predictions,
+metrics, run reports, and upload-ready adapter artifacts.
+
+The scale-up path requires valid prepared episodes, held-out episode splits,
+training metadata, predictions, metrics, and a run report. A result is ready
+for public README, website, or Hugging Face updates only after the validator
+passes and `scripts/omni/package_verified_omni_result.py` creates a
+public-safe derived-artifact package. The current verified package is listed in
+[`docs/data/omni_finetune_verified_result.json`](docs/data/omni_finetune_verified_result.json).
+
+### Sample Count Decision
+
+Do not treat "10M" as a reason to start with the entire dataset. The engineering
+unit that matters first is diverse held-out episodes, not adjacent windows from
+one session.
+
+| Phase | Episodes/samples | Approx windows at stride 5 | Purpose |
+| --- | ---: | ---: | --- |
+| Readiness | 1-3 | 1k-3k | Verify loaders, token alignment, and task heads |
+| Pilot | 16-32 | 18k-37k | First held-out-episode evaluation |
+| Useful LoRA run | 64-128 | 74k-149k | Train sensor adapters plus selected Qwen3-Omni LoRA |
+| Storage-heavy run | 256+ | 297k+ | Only after download layout and checkpoint size are stable |
+
+Use the budget helper before downloading:
+
+```bash
+python scripts/omni/plan_finetune_sample_budget.py \
+ --storage-root /path/to/storage \
+ --target-free-after-download-gb 800 \
+ --all-training-per-episode-gb 2.4 \
+ --full-preview-per-episode-gb 5.1
+```
+
+### Multi-Episode Readiness Gate
+
+```bash
+python scripts/omni/discover_xperience10m_sources.py \
+ --workspace /path/to/ropedia-xperience-10m-task-suite \
+ --data-root /path/to/xperience10m_data \
+ --output results/omni_finetune/source_discovery.json
+```
+
+Current status in this repo:
+
+- public_sample_valid_episodes: 1 (degraded-valid: annotation + fisheye_cam0.mp4)
+- gated_metadata_audit: 12,102 complete visible episodes across 802 complete sessions
+- selected_episode_plan: 128 source-balanced episodes, 96/16/16 train/val/test
+- selected_download_size: 277.71 GiB excluding `visualization.rrd`
+- verified_validation_aware_diagnostic_package: true
+- selected_split: 96 train / 16 validation / 16 held-out test episodes
+- exported_windows: 2,848 train / 512 validation / 448 test
+- validation_samples_used: 512
+- held_out_eval: 448 test windows from 14 exported test episodes
+- train_loss / val_loss: 0.4130 / 0.0331
+- current_quality_target: JSON validity 87.50%, below the 98% target
+- gated dataset: available for selected multi-episode data preparation
+- source_discovery: `results/omni_finetune/source_discovery.json`
+- data_status: `results/omni_finetune/DATA_ACCESS_STATUS.md`
+- access_status: `results/omni_finetune/MULTI_EPISODE_ACCESS_STATUS.md`
+
+Use this gate before scheduling any full fine-tune run. The pilot should use
+balanced held-out selection, not the first paths in repository order. The
+current 128-episode selection filters for complete leaf episodes, excludes
+`visualization.rrd`, balances episode-size bands, and preserves one selected
+episode per top-level session UUID.
+
+### Progressive Train/Validation Pilot
+
+The selected 128-episode plan can be used before every episode has arrived by
+training only on prepared `train` episodes and monitoring prepared `val` episodes.
+The final `test` episodes stay sealed until the end, so early development does
+not contaminate held-out evaluation.
+
+```bash
+python scripts/omni/build_selection_episode_manifest.py \
+ --workspace /path/to/ropedia-xperience-10m-task-suite \
+ --data-root /path/to/xperience10m_128 \
+ --selection-json results/omni_finetune/xperience10m_128_episode_selection.json \
+ --output results/omni_finetune/trainval_progressive/episode_manifest_trainval.json \
+ --include-split train \
+ --include-split val
+```
+
+`scripts/omni/run_trainval_progressive_128.sh` wraps the same guard, exports a
+train/val-only Qwen3-Omni JSONL dataset, and launches LoRA training without
+running final test evaluation. The exporter uses session-qualified episode IDs
+and path-based split matching so repeated folder names such as `ep1` cannot
+collide across different sessions.
+
+For larger prepared subsets, `scripts/omni/run_trainval_parallel_export_8gpu.sh`
+uses the same split guard, exports episodes in parallel CPU shards, skips and
+reports episodes that contain no labeled windows under the configured label
+rule, then launches Qwen3-Omni LoRA with `NUM_PROCESSES=8`.
+
+### Full 128-Episode Held-Out Pilot
+
+Once all selected episodes are complete, use the fixed selected-episode split:
+
+- 96 train episodes,
+- 16 validation episodes,
+- 16 held-out test episodes.
+
+The clean full-run launcher validates the selected split, exports all splits in
+parallel, trains Qwen3-Omni LoRA on train episodes while optionally monitoring
+validation loss, then evaluates on the held-out test split:
+
+```bash
+RUN_ID=xperience10m_qwen3_omni_128ep_fullsplit_fast8gpu \
+DATA_ROOT=/path/to/xperience10m_128 \
+SELECTION_JSON=results/omni_finetune/xperience10m_128_episode_selection.json \
+MODEL_DIR=/path/to/Qwen__Qwen3-Omni-30B-A3B-Instruct \
+NUM_PROCESSES=8 \
+TRAIN_VAL_SPLIT=val \
+MAX_VAL_SAMPLES=512 \
+scripts/omni/run_128_fullsplit_parallel_export_8gpu.sh
+```
+
+The current verified diagnostic package uses the same selected split and 8-GPU
+training path, records validation loss over 512 validation windows, and keeps
+the held-out test split sealed for final evaluation. The next pass should keep
+this package contract while tightening JSON decoding, target formatting, and
+action/subtask error analysis.
+
+Monitor the run with:
+
+```bash
+python scripts/omni/monitor_omni_progress.py \
+ --run-id xperience10m_qwen3_omni_128ep_fullsplit_fast8gpu
+```
+
+The monitor reads training `progress.jsonl`, new evaluator partial-prediction
+progress, and legacy generation logs, so long held-out evals can still expose
+sample-level progress even before final metrics are written.
+
+Validate the run artifacts stage by stage:
+
+```bash
+python scripts/omni/validate_omni_finetune_run.py \
+ --run-id xperience10m_qwen3_omni_128ep_fullsplit_fast8gpu \
+ --require-stage manifest
+
+python scripts/omni/validate_omni_finetune_run.py \
+ --run-id xperience10m_qwen3_omni_128ep_fullsplit_fast8gpu \
+ --require-stage eval \
+ --min-json-validity 0.98
+```
+
+After the eval validator passes, create the public-safe result package:
+
+```bash
+python scripts/omni/package_verified_omni_result.py \
+ --dataset-run-id xperience10m_qwen3_omni_128ep_fullsplit_fast8gpu \
+ --train-run-id \
+ --eval-run-id
+```
+
+For long-running remote jobs, the packaging step can be watched automatically:
+
+```bash
+python scripts/omni/watch_verified_omni_package.py \
+ --dataset-run-id xperience10m_qwen3_omni_128ep_fullsplit_fast8gpu \
+ --train-run-id \
+ --eval-run-id
+```
+
+While waiting, the watcher can append `eval_progress_observed` events from
+partial prediction files or legacy generation logs. This keeps the package
+status file useful during long held-out evaluations.
+
+The package copies only small derived artifacts such as metrics, predictions,
+confusion matrices, run reports, manifests, validation summaries, and training
+metadata. The exact required eval files and primary metrics come from the
+selected backbone contract in `configs/omni_backbones`, so Qwen3-Omni,
+Cosmos-style world models, and VLA/policy branches can share the same verified
+publication gate once their model-specific evaluators exist. The package
+excludes raw Xperience-10M files, base-model weights, adapter or checkpoint
+weights, full checkpoints, and large archives.
+
+For hardware setups that can run multiple eval workers, the Qwen evaluator also
+supports deterministic sample shards:
+
+```bash
+python scripts/omni/eval_qwen3_omni_lora.py \
+ --dataset-jsonl results/omni_finetune/xperience10m_qwen3_omni_128ep_fullsplit_fast8gpu_dataset/dataset.jsonl \
+ --adapter-dir checkpoints//adapter_lora \
+ --run-id \
+ --eval-split test \
+ --sample-offset 0 \
+ --sample-stride 4
+
+python scripts/omni/merge_qwen3_omni_eval_shards.py \
+ --dataset-jsonl results/omni_finetune/xperience10m_qwen3_omni_128ep_fullsplit_fast8gpu_dataset/dataset.jsonl \
+ --output-dir results/omni_finetune/ \
+ --shard-dir results/omni_finetune/ \
+ --shard-dir results/omni_finetune/ \
+ --shard-dir results/omni_finetune/ \
+ --shard-dir results/omni_finetune/
+```
+
+Only the merged eval directory should be validated and reported publicly,
+because the merger checks coverage and recomputes the metrics from all
+held-out predictions.
+
+After dataset export, a model-neutral window index can be created for future
+backbones:
+
+```bash
+python scripts/omni/export_model_neutral_window_index.py \
+ --dataset-jsonl results/omni_finetune/xperience10m_qwen3_omni_128ep_fullsplit_fast8gpu_dataset/dataset.jsonl
+```
+
+This produces `window_index.jsonl` and `window_index_manifest.json` so Cosmos-
+style world models and VLA/policy branches can reuse the same split-checked
+windows without depending on Qwen chat-message records.
+
+### Uploading Qwen3-Omni LoRA artifacts
+
+The public-safe verified package intentionally excludes raw data, base Qwen
+weights, LoRA weights, and full checkpoints. Adapter upload is a separate step:
+use it only when the intended adapter directory is present and the model card
+clearly distinguishes older smoke weights from the selected-episode diagnostic
+or validation-aware run.
+
+```bash
+python3 scripts/omni/upload_qwen3_omni_lora_to_hf.py \
+ --repo-id cy0307/ropedia-qwen3-omni-lora-smoke \
+ --source-dir /path/to/adapter_upload_package \
+ --message "Upload Xperience-10M Qwen3-Omni LoRA pilot"
+```
+
+This script requires a valid Hugging Face token via `HF_TOKEN` or `--token`.
+Network availability to `huggingface.co` is required.
+
+### Foundation Backbone Plan
+
+The next modeling plan tracks several foundation-model branches instead of
+assuming one backbone solves every Xperience-10M objective.
+
+| Branch | Current role | When to use it |
+| --- | --- | --- |
+| Qwen3-Omni | First trainable multimodal LoRA pilot | Use for the selected 128-episode held-out baseline over video/audio/language plus sensor-bridge features. |
+| Cosmos 3 | First world-model/action-generation branch | Use after data preparation for future-window prediction, action-conditioned world modeling, and synthetic-data usefulness tests. |
+| GR00T | Humanoid/action-policy branch | Use after mocap/contact retargeting creates well-defined humanoid action targets. |
+| OpenVLA / openpi | Open VLA/policy baselines | Use after the project defines robot-compatible or action-token targets. |
+| Gemini Robotics | External reasoning reference | Use only for qualitative comparison or annotation support unless local trainable access exists. |
+| Xperience Embodied Foundation Model | Future Xperience-native pretraining goal | Use only after multi-episode pilots, full-corpus storage, distributed training infrastructure, and scaling evidence justify a from-scratch domain model. |
+
+See [`FOUNDATION_MODEL_PLAN.md`](FOUNDATION_MODEL_PLAN.md) and
+[`docs/data/foundation_model_plan.json`](docs/data/foundation_model_plan.json)
+for the full selection matrix, source links, and model-specific evaluation
+additions. See
+[`XPERIENCE_EMBODIED_FOUNDATION_MODEL_PRETRAINING.md`](XPERIENCE_EMBODIED_FOUNDATION_MODEL_PRETRAINING.md)
+for the long-term full-corpus pretraining plan.
+
+Backbone-specific contracts now live in [`configs/omni_backbones`](configs/omni_backbones).
+The extension contract is documented in
+[`OMNI_MODEL_EXTENSION_CONTRACT.md`](OMNI_MODEL_EXTENSION_CONTRACT.md), and the
+registry can be checked with:
+
+```bash
+python scripts/omni/backbone_registry.py --validate --json
+```
+
+Verify that every configured backbone can pass the public-safe packaging
+contract on synthetic derived artifacts:
+
+```bash
+python scripts/omni/smoke_test_backbone_packaging.py
+```
+
+After a real held-out package is created, audit it before updating README,
+website, or Hugging Face pages:
+
+```bash
+python scripts/omni/audit_verified_omni_package.py \
+ --package-dir results/omni_finetune/verified_public/
+```
+
+Create a new planned backbone branch from an existing contract template with:
+
+```bash
+python scripts/omni/scaffold_omni_backbone.py \
+ --template-backbone policy_vla_branch \
+ --id new_policy_branch \
+ --display-name "New Policy Branch" \
+ --model-family "Model family name" \
+ --dataset-contract xperience10m_observation_action_v1 \
+ --training-objective observation_to_action_policy \
+ --checkpoint-gate policy_checkpoint_action_space_and_normalizer \
+ --dry-run
+```
+
+Each backbone config declares the checkpoint gate, required train/eval files,
+allowed public artifacts, and forbidden private or heavyweight artifacts. This
+keeps Qwen3-Omni, Cosmos-style world models, and policy/VLA branches on the same
+split, validation, and publication discipline even though their training targets
+are different.
+
+## Additional Development Directions
+
+Beyond backbone selection and fine-tuning, Xperience-10M supports several
+concrete research-development tracks:
+
+| Direction | First useful artifact | Role in the project |
+| --- | --- | --- |
+| Episode taxonomy and data engine | Episode atlas, balance report, and split builder | Select representative data before training. |
+| Standardized benchmark protocol | Versioned train/val/test manifests and metric scripts | Make future model results comparable. |
+| Multimodal representation learning | Contrastive and masked-window encoder objectives | Learn reusable video/audio/depth/pose/mocap/IMU/language features. |
+| Skill and procedure graph mining | Step graph, transitions, preconditions, and effects | Connect perception to planning and long-horizon reasoning. |
+| Human-object affordance modeling | Contact, reachable-object, tool-use, and next-affordance tasks | Model what actions the scene makes possible. |
+| 3D/4D scene and object memory | Persistent scene/object maps from depth, pose, multiview video, and objects | Track world state beyond single frames. |
+| Data-quality and synchronization diagnostics | Per-episode QA for drift, missing streams, calibration, and corrupted files | Keep large multimodal training trustworthy. |
+| Policy, retargeting, and simulation transfer | Action-token conversion and robot-compatible imitation examples | Bridge human egocentric experience to robot policy work. |
+
+See [`ADDITIONAL_DEVELOPMENT_DIRECTIONS.md`](ADDITIONAL_DEVELOPMENT_DIRECTIONS.md)
+and [`docs/data/additional_development_directions.json`](docs/data/additional_development_directions.json).
+
+## Four Research Directions
+
+The 12 tasks are now organized against the four Ropedia research directions in
+a generated artifact, not only in prose:
+
+- [`research_direction_taxonomy.json`](results/episode_task_suite/research_directions/research_direction_taxonomy.json)
+- [`research_direction_task_map.csv`](results/episode_task_suite/research_directions/research_direction_task_map.csv)
+- [`research_direction_summary.md`](results/episode_task_suite/research_directions/research_direction_summary.md)
+- [`docs/data/research_directions.json`](docs/data/research_directions.json)
+
+The taxonomy uses two current baselines for every task:
+
+| Baseline | Role |
+| --- | --- |
+| Minimal interpretable heads | Softmax, logistic, ridge, and retrieval heads over the 8,546-dimensional multimodal representation. These expose the input/output contract cleanly. |
+| Neural MLP heads | Small PyTorch MLP classifiers/regressors on the same features and splits. These check whether nonlinear heads help before moving to Qwen/Omni fine-tuning. |
+
+Current direction-level coverage:
+
+| Direction | Current status | Covered task evidence | What is not solved yet |
+| --- | --- | --- | --- |
+| A. Human Modeling & Motion Understanding | Partially implemented | Hand Trajectory Forecasting and Contact State Prediction are direct; Action Recognition and Object Relevance Prediction are proxies. Neural MLP improves hand forecasting from `0.8647` to `0.1079` MPJPE. | No full body/shape model, SMPL/MANO target, deformation prior, or multi-episode motion-generation evaluation yet. |
+| B. 3D/4D Reconstruction & Neural Rendering | Proxy tasks only | Cross-Modal Retrieval, Cross-Modal Reconstruction, and Multimodal Synchronization Detection test alignment/reconstruction prerequisites. | No NeRF, Gaussian Splatting, TSDF, mesh, novel-view synthesis, or calibrated 4D reconstruction model yet. |
+| C. Egocentric Vision & Interaction | Strongest implemented track | 6 direct tasks: action, subtask, transition, next-action, object relevance, and caption grounding, plus alignment/order diagnostics and audio ablation. | Single-episode chronological split limits generalization; stronger audio and video-language backbones still need multi-episode testing. |
+| D. Scene Reconstruction & World Modeling | Early proxy tasks | Procedure Step Recognition, Next-Action Prediction, Object Relevance Prediction, Cross-Modal Retrieval, Cross-Modal Reconstruction, Temporal Order Verification, and Multimodal Synchronization Detection provide state/world-model probes. | No persistent scene graph, object permanence task, long-term map, or held-out-episode world model yet. |
+
+The important interpretation is that all four directions can be **started** from
+the Xperience-10M sample modalities, but only direction C is strongly represented
+by the current 12-task suite. Directions A, B, and D need additional targets and
+multi-episode training before they become full research deliverables.
+
+## Four Direction-Extension Probes
+
+Beyond the original 12 core tasks, the repo now includes one extra data-backed
+probe for each research direction. These probes are computed from the same
+`shared_windows.npz`, `windows.csv`, and `feature_manifest.json` artifacts, so
+the reported numbers are computed from sample-derived features and saved metric artifacts.
+
+- [`research_direction_extension_results.json`](results/episode_task_suite/research_direction_extensions/research_direction_extension_results.json)
+- [`research_direction_extension_summary.md`](results/episode_task_suite/research_direction_extensions/research_direction_extension_summary.md)
+- [`docs/data/research_direction_extensions.json`](docs/data/research_direction_extensions.json)
+- [`research_direction_extension_tasks.svg`](docs/assets/charts/research_direction_extension_tasks.svg)
+
+
+
+| Direction | New extension task | Input | Output | Minimal | Neural MLP | Why it matters |
+| --- | --- | --- | --- | ---: | ---: | --- |
+| A. Human Modeling & Motion Understanding | Body and Hand Motion Intensity | non-mocap video/depth/pose/IMU/SLAM/language features | high vs low body/hand motion | `0.7827` macro-F1 | `0.7986` macro-F1 | Starts a human-motion-energy target without leaking mocap input. |
+| B. 3D/4D Reconstruction & Neural Rendering | Multi-View Consistency Retrieval | fisheye camera feature query | synchronized stereo-left view rank | `0.5534` MRR | `0.3469` MRR | Tests whether multi-view features preserve synchronized 4D scene identity. |
+| C. Egocentric Vision & Interaction | Action Phase Progress Estimation | non-caption multimodal window | progress inside current action segment | `0.3416` MAE | `0.3038` MAE | Adds a task-structure/intent-style target beyond class labels. |
+| D. Scene Reconstruction & World Modeling | Short-Horizon Ego-Motion Forecasting | current sensors excluding camera translation and captions | future camera-translation delta | `0.1989` MAE | `0.0989` MAE | Starts a short-horizon world-model target over wearer motion. |
+
+Run:
+
+```bash
+python scripts/research_direction_extension_tasks.py
+```
+
+These four probes make the four-direction mapping more concrete, but they are
+still single-episode extension baselines. Full research conclusions still require
+multi-episode training, held-out episode evaluation, and stronger task-specific
+models.
+
+## Task Walkthroughs For Juniors
+
+Every task now has a beginner-facing explanation with:
+
+- a concrete coffee-episode case study,
+- exact input contract,
+- middle process modules,
+- output contract,
+- minimal and neural metric,
+- one important limitation.
+
+Primary files:
+
+- [`TASK_WALKTHROUGHS.md`](results/episode_task_suite/task_walkthroughs/TASK_WALKTHROUGHS.md)
+- [`task_walkthroughs.json`](results/episode_task_suite/task_walkthroughs/task_walkthroughs.json)
+- [`docs/data/task_walkthroughs.json`](docs/data/task_walkthroughs.json)
+- [`docs/data/task_surface_integrity.json`](docs/data/task_surface_integrity.json)
+
+Compact map:
+
+| Task | Case study | Input -> process -> output |
+| --- | --- | --- |
+| Action Recognition | A pouring window should be named as the current action. | all-modality window -> action label builder + classifier -> action class |
+| Procedure Step Recognition | A fine action is grouped into a broader drink-preparation stage. | all-modality window -> subtask label builder + classifier -> subtask label |
+| Action Boundary Detection | Detect the change from preparing to pouring. | window -> boundary builder + binary classifier -> boundary/steady |
+| Next-Action Prediction | A preparing window predicts what happens 20 frames later. | current window -> future-label shift + classifier -> next action |
+| Hand Trajectory Forecasting | A hand moving toward a cup becomes a future 3D hand path. | current window -> future mocap target + regressor -> hand trajectory |
+| Contact State Prediction | Decide whether hand/body contact is happening. | non-contact features -> contact target + binary classifier -> contact label |
+| Object Relevance Prediction | Infer milk, cup, coffee, or related objects during pouring. | non-caption features -> multi-hot object target + sigmoid heads -> object set |
+| Language Grounding | Query Pour milk into coffee and retrieve the matching moment. | text-like query + candidates -> projection + cosine ranker -> ranked windows |
+| Cross-Modal Retrieval | Motion/IMU from pouring retrieves matching depth/video. | motion/IMU/camera -> projection + candidate index -> ranked depth/video windows |
+| Cross-Modal Reconstruction | Infer depth/video features from motion, IMU, and camera pose. | source modalities -> scaler + regressor -> target modality vector |
+| Temporal Order Verification | Tell whether reaching then pouring was reversed. | adjacent window pair -> pair combiner + binary classifier -> correct/reversed |
+| Multimodal Synchronization Detection | Catch motion paired with visual/depth features shifted in time. | motion side + visual side -> aligned/shifted pair builder + classifier -> aligned/shifted |
+
+## Minimal 12-Task Architectures
+
+These are deliberately minimal baselines. They are useful because every
+input/output contract is explicit, not because they are strong embodied-AI
+models.
+
+Shared setup:
+
+```text
+raw episode -> 20-frame windows, stride 5 -> 8,546-dimensional multimodal representation
+chronological split: first 70% train, last 30% test
+scalers are fit on train windows only
+```
+
+There are four reusable head families:
+
+| Head family | Used by | What it means |
+| --- | --- | --- |
+| Linear softmax classifier | Action Recognition, Procedure Step Recognition, Action Boundary Detection, Next-Action Prediction, Contact State Prediction, Temporal Order Verification, Multimodal Synchronization Detection | z-score features, then `XW+b`, softmax, cross-entropy, L2 |
+| Dual ridge regression/projection | Hand Trajectory Forecasting, Cross-Modal Reconstruction | z-score input/target, solve ridge regression with L2=10 |
+| Ridge + cosine ranking | Language Grounding, Cross-Modal Retrieval | project one modality into another feature space, then rank candidates by cosine |
+| Multi-label logistic regression | Object Relevance Prediction | z-score non-caption features, sigmoid object heads, threshold at 0.5 |
+
+The optional neural run keeps the same window representation, leakage filters,
+chronological splits, and metrics, but replaces the task heads with small
+PyTorch MLP classifiers or regressors. Its outputs live under
+[`results/episode_task_suite/neural_mlp/`](results/episode_task_suite/neural_mlp/),
+and the rollup is stored in the `neural_tasks` section of
+[`results/episode_task_suite/summary_report.json`](results/episode_task_suite/summary_report.json).
+
+The task-specific heads are:
+
+| Task | Input | Minimal head | Output |
+| --- | --- | --- | --- |
+| Action Recognition | all featurized modalities | linear softmax | current action class |
+| Procedure Step Recognition | all featurized modalities | linear softmax | current subtask class |
+| Action Boundary Detection | all featurized modalities | linear softmax | steady vs action boundary |
+| Next-Action Prediction | all featurized modalities at `t` | linear softmax | action at `t+20` frames |
+| Hand Trajectory Forecasting | all featurized modalities at `t` | ridge regression | future 10-frame left/right hand joints |
+| Contact State Prediction | non-contact and non-caption signals | linear softmax | any body contact |
+| Object Relevance Prediction | non-caption signals | multi-label logistic | relevant object set |
+| Language Grounding | sensor windows projected to text space | ridge projection + cosine ranking | matching time window for text query |
+| Cross-Modal Retrieval | motion/IMU/camera projected to visual space | ridge projection + cosine ranking | matching depth/video window |
+| Cross-Modal Reconstruction | motion/IMU/camera | ridge regression | compressed depth/video target |
+| Temporal Order Verification | `[x_t, x_t+1, x_t+1-x_t]` | binary linear softmax | correct vs reversed order |
+| Multimodal Synchronization Detection | motion plus visual pair | binary linear softmax | aligned vs shifted by 8 windows |
+
+## Key Results
+
+| Experiment | Main score | Accuracy | Notes |
+| --- | ---: | ---: | --- |
+| Motion-only action | 0.9688 macro-F1 | 0.9828 | Uses motion/IMU features only |
+| Current all-feature action | 0.9829 macro-F1 | 0.9863 | 8,546-dimensional multimodal representation |
+| Motion-only subtask | 0.9528 macro-F1 | 0.9759 | Strong within-episode subtask signal |
+| Current all-feature subtask | 0.9173 macro-F1 | 0.9828 | High accuracy, lower class-balanced score |
+| Cross-modal retrieval | 0.3678 top-5 | n/a | Motion/IMU/camera/audio retrieves matching depth/video |
+| Transition detection | 0.6118 macro-F1 | 0.9080 | Boundary F1 is 0.1250 |
+| Hand trajectory forecast | 0.8647 MPJPE | n/a | Predicts future hand-joint trajectory |
+| Neural MLP hand forecast | 0.1079 MPJPE | n/a | Same features/split, nonlinear regression head |
+| Neural MLP temporal order | 0.8520 F1 | 0.8578 | Strong improvement on adjacent-window ordering |
+| Neural MLP misalignment | 0.7153 F1 | 0.7009 | Detects shifted motion/visual/audio pairs better than the linear head |
+| Audio ablation | +0.0418 mean delta | n/a | Current audio variant improves the primary metric on 6 of 12 task contracts |
+| Alternate audio representation | +0.0936 mean delta | n/a | Alternate audio-window representation improves over the baseline audio variant on 6 of 12 task contracts |
+
+## Audio Contribution Study
+
+The audio ablation keeps the same windows and task labels, then compares input
+variants under the same chronological split. The script
+[`scripts/audio_ablation_and_raw_upgrade.py`](scripts/audio_ablation_and_raw_upgrade.py)
+reuses the real task-suite windows and evaluates six variants for
+every task: current inputs, no audio, audio-only, alternate audio-only, audio
+representation replacement, and all inputs plus the alternate audio representation.
+
+The measured single-episode result is task-specific:
+
+| Readout | Value |
+| --- | ---: |
+| Tasks where current audio improves the primary metric | 6 / 12 |
+| Mean current-audio delta | +0.0418 |
+| Tasks where alternate audio representation improves over baseline audio | 6 / 12 |
+| Mean alternate-representation delta vs baseline audio | +0.0936 |
+
+Full files:
+
+- [`results/audio_ablation/AUDIO_ABLATION_SUMMARY.md`](results/audio_ablation/AUDIO_ABLATION_SUMMARY.md)
+- [`results/audio_ablation/audio_ablation_metrics.csv`](results/audio_ablation/audio_ablation_metrics.csv)
+- [`results/audio_ablation/audio_delta_summary.csv`](results/audio_ablation/audio_delta_summary.csv)
+- [`docs/data/audio_ablation_summary.json`](docs/data/audio_ablation_summary.json)
+- [`docs/assets/charts/audio_ablation_delta.svg`](docs/assets/charts/audio_ablation_delta.svg)
+
+## Neural MLP Results
+
+The neural baseline was run locally with `--include-neural` for all 12 tasks
+using 80 epochs, hidden size 128, batch size 128, and CPU execution. It is not a
+foundation model result; it is a controlled nonlinear-head comparison over the
+same 8,546-dimensional multimodal representation.
+
+| Task | Neural metric | Minimal metric | Readout |
+| --- | ---: | ---: | --- |
+| Action Recognition | 0.0148 macro-F1 | 0.0500 macro-F1 | Still blocked by unseen future classes |
+| Procedure Step Recognition | 0.0281 macro-F1 | 0.0506 macro-F1 | Same single-episode split limitation |
+| Action Boundary Detection | 0.5862 macro-F1 | 0.6118 macro-F1 | Similar to the linear baseline |
+| Next-Action Prediction | 0.0419 macro-F1 | 0.0593 macro-F1 | Same unseen-label issue |
+| Hand Trajectory Forecasting | 0.1079 MPJPE | 0.8647 MPJPE | Neural regression improves this target |
+| Contact State Prediction | 1.0000 macro-F1 | 1.0000 macro-F1 | Degenerate one-class sample |
+| Object Relevance Prediction | 0.1679 micro-F1 | 0.1803 micro-F1 | Similar weak object signal |
+| Language Grounding | 0.0168 MRR | 0.0160 MRR | Similar ranking behavior |
+| Cross-Modal Retrieval | 0.1300 MRR | 0.2693 MRR | Linear ridge remains stronger here |
+| Cross-Modal Reconstruction | -0.0102 R2 | -0.0153 R2 | Small improvement but still weak |
+| Temporal Order Verification | 0.8520 F1 | 0.5400 F1 | Neural head captures local temporal structure |
+| Multimodal Synchronization Detection | 0.7153 F1 | 0.5052 F1 | Neural head improves alignment detection |
+
+The strongest single-episode self-supervised signal is cross-modal retrieval:
+motion/IMU/camera/audio features retrieve matching depth/video windows substantially
+better than random.
+
+## Single-Episode Diagnostics and Explorer
+
+While waiting for broader Xperience-10M access, the repo now includes an
+artifact-driven diagnostics pass over the public sample episode:
+
+- `results/single_episode_diagnostics/object_labels/window_object_labels.csv`
+ exports 1,161 real window-level object-label sets from `annotation.hdf5`.
+- `results/single_episode_diagnostics/modality_ablation/ablation_metrics.csv`
+ recomputes all 96 task/modality cells, including object relevance.
+- `results/single_episode_diagnostics/timeline_overlay/timeline_overlay.csv`
+ aligns 2,079 existing prediction rows back to the episode timeline.
+- `results/single_episode_diagnostics/alignment_stress/alignment_shift_metrics.csv`
+ evaluates cross-modal retrieval under explicit time shifts.
+- `docs/single_episode_explorer.html` is a static interactive page for
+ inspecting window labels, objects, predictions, modality statistics, and
+ diagnostic scores.
+
+These are single-episode research diagnostics. They are useful for studying
+task definitions, feature behavior, and model errors before scaling to more
+episodes; they are not reported as multi-episode benchmark results.
+
+## Reproducibility Check
+
+I re-ran the full pipeline from the local raw public sample into a temporary
+local workspace and compared regenerated metrics with the committed
+artifacts. The baseline metrics, 12 task metrics, feature manifest, and
+available modality manifest matched exactly after float normalization.
+
+See [`notes/reproducibility_audit.md`](notes/reproducibility_audit.md) for the
+commands and verification evidence.
+
+## Why Some Scores Are Low
+
+The task suite intentionally uses a chronological split:
+
+```text
+first 70% of the episode -> train
+last 30% of the episode -> test
+```
+
+The test segment contains some action/subtask labels never seen during training.
+Timeline and next-action classifiers therefore expose the core limitation of
+single-episode learning instead of hiding it behind random splits.
+
+## Modalities Used
+
+The current public-sample pipeline uses:
+
+- hand/body mocap joints and contact labels,
+- camera translation and rotation,
+- IMU acceleration and gyroscope traces,
+- depth confidence features,
+- six video streams,
+- audio from the sample MP4 stream,
+- caption/object/interaction text features,
+- SLAM point-cloud summary features,
+- calibration parameters.
+
+The full technical source manifest is stored in
+[`results/episode_task_suite/feature_manifest.json`](results/episode_task_suite/feature_manifest.json).
+
+## Data Notice
-The official full dataset is
-[`ropedia-ai/xperience-10m`](https://huggingface.co/datasets/ropedia-ai/xperience-10m),
-and the public sample source is
-[`ropedia-ai/xperience-10m-sample`](https://huggingface.co/datasets/ropedia-ai/xperience-10m-sample).
-This artifact repo contains derived research outputs from the sample episode and
-selected-pilot preparation files. Raw Xperience-10M videos, annotations, RRD
-visualizations, gated data, and full Qwen weights are not redistributed.
-
-For readers who need source provenance, the detailed dataset-card alignment
-notes are included in `XPERIENCE10M_DATASET_CARD_ALIGNMENT.md` and
-`SOURCE_ALIGNMENT_AUDIT.md`. Most readers can start with the dashboard,
-task results, and related model repositories above.
-
-## Future Foundation-Model Goal
-
-The included `XPERIENCE_EMBODIED_FOUNDATION_MODEL_PRETRAINING.md` describes a
-future **Xperience Embodied Foundation Model**: a domain-specific model
-pretrained from scratch over full-corpus Xperience-10M video, audio, depth,
-pose/SLAM, mocap, IMU, and language streams after smaller multi-episode pilots
-prove value and the required storage/compute path exists. This is a research
-roadmap item, not a completed artifact in this dataset repo.
+Xperience-10M data belongs to its original authors and is subject to the
+official Ropedia dataset license and access terms. This repo contains code and
+derived experiment artifacts only; it does not redistribute the raw videos or
+raw annotation dataset.
diff --git a/RESEARCH_ROADMAP.md b/RESEARCH_ROADMAP.md
index d72b733a95d36398bea87782d418535c23d19f70..d3341d0b40473fc4e9621c056ce2f51cb7d10a9e 100644
--- a/RESEARCH_ROADMAP.md
+++ b/RESEARCH_ROADMAP.md
@@ -10,26 +10,29 @@ should exist before the stage is treated as complete.
| Stage | Status | Entry condition | Research deliverables | Completion evidence |
| --- | --- | --- | --- | --- |
| Public-Sample Task Lab | Implemented | One public Xperience-10M sample episode is available. | 1,161 aligned windows, 12 task contracts, minimal heads, neural MLP heads, modality atlas, task walkthroughs, and derived figures. | `PROJECT_STATUS.md`, `EVALUATION_PROTOCOL.md`, `RESEARCH_TAKEAWAYS.md`, `docs/data/summary_metrics.json`, `results/episode_task_suite/summary_report.json` |
-| Multi-Episode Data Preparation | Active | Gated dataset availability and enough storage for selected episodes. | 128 selected episodes, episode manifest, missing-view manifest, held-out episode split, and source-discovery report. | `results/omni_finetune/DATA_ACCESS_STATUS.md`, `results/omni_finetune/MULTI_EPISODE_ACCESS_STATUS.md`, `results/omni_finetune/source_discovery.json` |
-| Qwen3-Omni LoRA Pilot | Next | Selected episodes prepared locally with no train/test episode leakage. | Dataset JSONL/media manifests, LoRA adapter checkpoint, progress logs, held-out predictions, metrics, confusion matrices, and run report. | `dataset_manifest.json`, `training_metadata.json`, `progress.jsonl`, `metrics.json`, `predictions.jsonl`, `RUN_REPORT.md` |
-| Foundation-Model Selection Matrix | Next | The selected pilot episodes are prepared, or a 3-8 episode dry run is available for preprocessing checks. | Backbone registry, Cosmos 3 world-model branch plan, Qwen3-Omni baseline plan, OpenVLA/openpi/GR00T policy candidates, and model-specific evaluation additions. | `FOUNDATION_MODEL_PLAN.md`, `docs/data/foundation_model_plan.json`, `research_roadmap_interactive.json` |
-| 64-128 Episode Robustness Run | Planned | The selected-episode pilot trains and evaluates cleanly. | Split-by-session metrics, modality ablations, calibration/object/language error analysis, and sensitivity to missing views. | Held-out metrics by session, task, and modality; ablation tables; qualitative error analysis. |
+| Multi-Episode Data Preparation | Implemented for first selected pilot | Gated dataset availability and enough storage for selected episodes. | 128 selected episodes, episode manifest, missing-view manifest, held-out episode split, and source-discovery report. | `results/omni_finetune/DATA_ACCESS_STATUS.md`, `results/omni_finetune/MULTI_EPISODE_ACCESS_STATUS.md`, `results/omni_finetune/xperience10m_128_episode_selection.json` |
+| Qwen3-Omni LoRA Validation-Aware Diagnostic Pilot | Verified baseline | Selected episodes prepared locally with no train/test episode leakage. | Dataset JSONL/media manifests, LoRA adapter checkpoint, progress logs, validation monitoring, held-out predictions, metrics, confusion matrices, and run report. | `docs/data/omni_finetune_verified_result.json`, `results/omni_finetune/verified_public/`, `metrics.json`, `predictions.jsonl`, `RUN_REPORT.md` |
+| Structured-Output And Error-Analysis Pass | Active next step | The validation-aware diagnostic package exists and shows weak held-out quality. | Same 96/16/16 split, stricter JSON decoding or target formatting, action/subtask error analysis, held-out test evaluation, and comparison to the verified validation-aware baseline. | Updated quality-target report, error-analysis tables, held-out metrics, and verified public package. |
+| Foundation-Model Selection Matrix | Current | The selected pilot episodes are prepared, or a 3-8 episode dry run is available for preprocessing checks. | Backbone registry, Cosmos 3 world-model branch plan, Qwen3-Omni baseline plan, OpenVLA/openpi/GR00T policy candidates, and model-specific evaluation additions. | `FOUNDATION_MODEL_PLAN.md`, `docs/data/foundation_model_plan.json`, `research_roadmap_interactive.json` |
+| 64-128 Episode Robustness Run | Planned | The validation-aware selected-episode pilot trains and evaluates cleanly. | Split-by-session metrics, modality ablations, calibration/object/language error analysis, and sensitivity to missing views. | Held-out metrics by session, task, and modality; ablation tables; qualitative error analysis. |
| Cosmos 3 and Policy-Model Extensions | Planned | Enough multi-episode data, compute budget, and model-specific action/world-state targets. | Cosmos 3 future-window or action-conditioned world-model probes, OpenVLA/openpi/GR00T action-policy baselines, modality-conditioning checks, affordance tasks, and synthetic-data usefulness tests. | Task-specific held-out evaluations, qualitative inspection, and updated model cards. |
| Xperience Embodied Foundation Model Pretraining | Future | Full-corpus access, PB-scale storage path, multi-node compute, and positive scaling evidence from smaller runs. | Xperience-native temporal multimodal model, full-corpus manifests, pretraining shards, scaling curves, held-out evaluations, and model card. | Pretraining metadata, checkpoint inventory, held-out metrics, scaling report, and data-boundary report. |
## Current Decision Point
-The useful next decision is data scale plus backbone fit: keep the public-sample
-task suite as the development harness, stage enough official Xperience-10M
-episodes to run the held-out Qwen3-Omni pilot, then choose larger model branches
-by task fit. Qwen3-Omni remains the first trainable multimodal LoRA target.
-Cosmos 3 becomes the first world-model/action-generation branch. OpenVLA,
-openpi, GR00T, Octo, and SmolVLA-style models become policy/action branches only
-after the action target is explicit. A from-scratch Xperience Embodied
-Foundation Model is the long-term native-pretraining goal, not the immediate
-experiment. The public sample is already enough for task design, feature
-contracts, walkthroughs, and baseline comparisons. It is not enough to measure
-general embodied-AI model quality.
+The useful next decision is model-quality improvement plus backbone fit: keep
+the public-sample task suite as the development harness, use the verified
+Qwen3-Omni validation-aware diagnostic pilot as the first cross-episode
+baseline, then improve format reliability and task quality before claiming
+model quality.
+Qwen3-Omni remains the first trainable multimodal LoRA target. Cosmos 3 becomes
+the first world-model/action-generation branch. OpenVLA, openpi, GR00T, Octo,
+and SmolVLA-style models become policy/action branches only after the action
+target is explicit. A from-scratch Xperience Embodied Foundation Model is the
+long-term native-pretraining goal, not the immediate experiment. The public
+sample is already enough for task design, feature contracts, walkthroughs, and
+baseline comparisons. The first multi-episode pilot is enough to verify the
+end-to-end training loop, but its weak metrics are not final model quality.
## Additional Concrete Development Directions
@@ -38,7 +41,7 @@ depend on immediately training a larger foundation model:
| Direction | First artifact | Research value |
| --- | --- | --- |
-| Episode taxonomy and data engine | Episode atlas, category tags, balance report, and split builder. | Makes episode selection representative and auditable. |
+| Episode taxonomy and data engine | Episode atlas, category tags, balance report, and split builder. | Makes episode selection representative and measurable. |
| Standardized benchmark protocol | Fixed splits, task cards, metric scripts, and leakage checks. | Makes future model comparisons fair. |
| Multimodal representation learning | Contrastive and masked-window objectives over synchronized modalities. | Learns reusable encoders before expensive large-model training. |
| Skill and procedure graph mining | Steps, transitions, preconditions, effects, and temporal skill graphs. | Connects perception to planning and long-horizon reasoning. |
@@ -72,7 +75,8 @@ Evidence to inspect:
This stage expands the same data contract to official gated episodes. The key
research requirement is episode-level separation: training and test examples
must come from different episodes, not different windows inside the same
-episode.
+episode. The first selected 96/16/16 split has been used for a verified
+Qwen3-Omni diagnostic pilot.
Evidence to inspect:
@@ -84,8 +88,11 @@ Evidence to inspect:
### 3. Qwen3-Omni LoRA Pilot
This stage uses Qwen3-Omni as the multimodal backbone and trains lightweight
-LoRA adapters. The first target is a complete held-out-episode training and
-evaluation loop with inspectable manifests, predictions, and metrics.
+LoRA adapters. The first held-out diagnostic package now exists. It proves the
+export, training, evaluation, validation, and public-safe packaging loop, but
+the metrics are weak: JSON validity is 87.50%, action macro-F1 is 0.0027, and
+subtask accuracy is 0.0067. Treat it as a baseline and error-analysis starting
+point.
Expected outputs:
diff --git a/RESEARCH_TAKEAWAYS.md b/RESEARCH_TAKEAWAYS.md
index e603815af485cb5bb28b53a87ee8076d8243cd40..15360c85a0cbcd051206daf183dc8cb71956827b 100644
--- a/RESEARCH_TAKEAWAYS.md
+++ b/RESEARCH_TAKEAWAYS.md
@@ -97,17 +97,18 @@ Current scope: This is a single-episode ablation over fixed ridge heads. It vali
### The next scientific unit is held-out episodes, not more adjacent windows
-The prepared Qwen3-Omni path now targets a selected 128-episode pilot; held-out metrics will be reported after staging, training, and evaluation complete.
+The selected Qwen3-Omni path now has a verified validation-aware held-out diagnostic pilot. It proves the cross-episode train/validation/eval loop, but the weak metrics show that structured-output reliability and task-quality error analysis are the next modeling problems.
| Metric | Value |
| --- | ---: |
-| `target_episodes` | 128 |
-| `selected_sessions` | 128 |
-| `valid_candidates` | 12,102 |
+| `selected_episodes` | 128 |
+| `held_out_test_windows` | 448 |
+| `json_validity_rate` | 0.8750 |
+| `action_macro_f1` | 0.0027 |
-Source: `results/omni_finetune/MULTI_EPISODE_ACCESS_STATUS.md`.
+Source: `docs/data/omni_finetune_verified_result.json`.
-Current scope: The selected-episode Qwen3-Omni fine-tune requires completed data preparation and held-out evaluation; the 32-episode Qwen3-Omni fine-tune requires gated data preparation before any real held-out metric is reported.
+Current scope: The selected-episode Qwen3-Omni validation-aware diagnostic pilot is verified, but held-out quality is still weak and JSON validity remains below the 98% target.
## How To Read These Results
diff --git a/docs/data/evaluation_protocol.json b/docs/data/evaluation_protocol.json
index b406fe9e708bc0e530648bdca7690609b74bda6f..4e0cb77e7fa5d19bb8f2535f7716d55f400580c4 100644
--- a/docs/data/evaluation_protocol.json
+++ b/docs/data/evaluation_protocol.json
@@ -2,7 +2,7 @@
"title": "Ropedia Xperience-10M Task Suite Evaluation Protocol",
"status": "pass",
"version": "2026-06-01",
- "generated_at_utc": "2026-06-04T16:42:13+00:00",
+ "generated_at_utc": "2026-06-06T13:49:32+00:00",
"source_files": [
"docs/data/summary_metrics.json",
"results/episode_task_suite/summary_report.json",
@@ -303,22 +303,23 @@
"Report unseen test classes when the chronological split exposes labels absent from the train segment."
],
"current_limitations": [
- "Cross-episode generalization is evaluated in the later multi-episode stage.",
+ "Cross-episode generalization for Qwen3-Omni has a first verified diagnostic pilot, but strong model quality is not yet shown.",
"Feature-vector reconstruction is separate from pixel depth, mesh, NeRF, or Gaussian reconstruction.",
- "Qwen3-Omni setup artifacts are preparation artifacts until the selected held-out pilot runs.",
+ "The verified validation-aware Qwen3-Omni diagnostic pilot has weak held-out metrics and needs structured-output and task-quality improvements before larger model-quality claims.",
"Full audio-visual representation learning still needs multi-episode training; the current report includes single-episode audio/no-audio ablations."
],
"scale_up_gate": {
- "required_before_full_omni_pilot": [
+ "required_before_next_omni_quality_pilot": [
"selected prepared Xperience-10M episodes",
"held-out episode split with no train/test episode leakage",
+ "validation samples during training",
"manifest, training metadata, progress logs, metrics, predictions, and run report",
"held-out evaluation on test episodes rather than train windows"
],
- "current_status": "prepared; selected data relay in progress",
+ "current_status": "verified diagnostic pilot; quality target not met",
"evidence": [
- "results/omni_finetune/DATA_ACCESS_STATUS.md",
- "results/omni_finetune/MULTI_EPISODE_ACCESS_STATUS.md"
+ "docs/data/omni_finetune_verified_result.json",
+ "results/omni_finetune/verified_public/"
]
}
}
diff --git a/docs/data/foundation_model_plan.json b/docs/data/foundation_model_plan.json
index 26eb23f76f69f17aa4ed4b75725a4315c19f39c1..d24e641e97b8283ddec4af880851e755f2b76aa0 100644
--- a/docs/data/foundation_model_plan.json
+++ b/docs/data/foundation_model_plan.json
@@ -1,7 +1,7 @@
{
"title": "Xperience-10M Foundation Model Plan",
"status": "planning_artifact",
- "current_boundary": "No held-out multi-episode foundation-model result has been completed in this repo. The current foundation-model artifacts are setup-stage until enough valid episodes are prepared and evaluated.",
+ "current_boundary": "A first held-out multi-episode Qwen3-Omni diagnostic pilot is verified in this repo, but it is not a strong model result. The current foundation-model work should treat it as the baseline train/eval/package loop before validation-aware Qwen reruns, Cosmos-style world modeling, or policy/VLA branches.",
"backbone_registry": {
"config_dir": "configs/omni_backbones",
"validator": "scripts/omni/backbone_registry.py --validate --json",
@@ -206,7 +206,7 @@
{
"step": 2,
"name": "First held-out baseline",
- "action": "Run Qwen3-Omni LoRA to establish the full train/eval loop."
+ "action": "Run validation-aware Qwen3-Omni LoRA to improve the verified diagnostic baseline."
},
{
"step": 3,
diff --git a/docs/data/omni_finetune_verified_result.json b/docs/data/omni_finetune_verified_result.json
new file mode 100644
index 0000000000000000000000000000000000000000..3311bc1b73851e940eeb44478f2aa8e027f99bc4
--- /dev/null
+++ b/docs/data/omni_finetune_verified_result.json
@@ -0,0 +1,78 @@
+{
+ "title": "Verified Qwen3-Omni LoRA Validation-Aware Held-Out Pilot",
+ "status": "verified_validation_aware_diagnostic_pilot",
+ "status_date": "2026-06-06",
+ "backbone": "Qwen/Qwen3-Omni-30B-A3B-Instruct",
+ "adapter": "Qwen3-Omni LoRA",
+ "dataset": "Ropedia Xperience-10M selected 128-episode pilot",
+ "split_policy": {
+ "unit": "episode",
+ "selected_episode_counts": {
+ "train": 96,
+ "val": 16,
+ "test": 16
+ },
+ "exported_window_counts": {
+ "train": 2848,
+ "val": 512,
+ "test": 448
+ },
+ "exported_episode_counts": {
+ "train": 89,
+ "val": 16,
+ "test": 14
+ },
+ "skipped_selected_episodes": 9,
+ "leakage_policy": "Train, validation, and test are separated by episode/session; test windows are used only for held-out evaluation."
+ },
+ "training": {
+ "num_processes": 8,
+ "epochs": 1,
+ "lora_rank": 16,
+ "lora_alpha": 32,
+ "lora_dropout": 0.05,
+ "num_train_samples": 2848,
+ "num_val_samples": 512,
+ "history": [
+ {
+ "epoch": 1,
+ "train_loss": 0.41304643672440994,
+ "val_loss": 0.0330660454928875,
+ "global_step": 356
+ }
+ ],
+ "loss": "answer-token cross entropy over supervised JSON tokens",
+ "note": "This validation-aware run uses the selected validation split during training and preserves the held-out test split for final evaluation."
+ },
+ "evaluation": {
+ "split": "test",
+ "num_samples": 448,
+ "held_out_episode_count": 14,
+ "json_validity_rate": 0.875,
+ "action_macro_f1": 0.0026621494447581404,
+ "subtask_accuracy": 0.006696428571428571,
+ "transition_accuracy": 0.8504464285714286,
+ "next_action_accuracy": 0.024553571428571428,
+ "contact_accuracy": 0.6450892857142857,
+ "object_micro_f1": 0.22299431459254582,
+ "quality_target": {
+ "json_validity_rate": 0.98,
+ "status": "not_met"
+ },
+ "previous_diagnostic_json_validity_rate": 0.8526785714285714
+ },
+ "interpretation": "This is a real held-out multi-episode validation-aware diagnostic pilot proving the export, LoRA training with validation monitoring, evaluation, validation, and public-safe packaging loop. JSON validity improved over the earlier no-validation diagnostic run, but task-quality metrics remain weak, so it should be used as a baseline and error-analysis starting point rather than a strong Xperience-10M model.",
+ "public_package": {
+ "path": "results/omni_finetune/verified_public/xperience10m_qwen3_omni_128ep_96train_16val_16test_valmon_20260605_eval",
+ "audit_status": "pass",
+ "contains_raw_xperience10m_data": false,
+ "contains_qwen_base_weights": false,
+ "contains_lora_weights": false
+ },
+ "required_next_steps": [
+ "Improve JSON-format reliability through prompt, decoding, constrained parsing, or target formatting changes.",
+ "Add error analysis by episode, action family, object category, and missing-modality state.",
+ "Run a second validation-aware Qwen3-Omni pass only after the JSON/output contract is tightened.",
+ "Keep the same verified package contract for Cosmos-style world-model and VLA/policy branches."
+ ]
+}
diff --git a/docs/data/project_status.json b/docs/data/project_status.json
index ef49d228d0d09819af36f6454c8c1fce68bbc8b9..c2b1591a863490f6328fc5e0180a5f76c1c1dbc5 100644
--- a/docs/data/project_status.json
+++ b/docs/data/project_status.json
@@ -1,8 +1,8 @@
{
"title": "Ropedia Xperience-10M Task Suite Project Status",
"version": "2026-06-01",
- "decision": "public_sample_pipeline_verified_multi_episode_omni_data_staging",
- "research_positioning": "A research-engineering study that makes one public Xperience-10M sample episode inspectable, defines embodied-AI tasks over synchronized modalities, records baseline behavior, and keeps later multi-episode model-quality claims separate from current single-episode evidence.",
+ "decision": "public_sample_pipeline_verified_qwen3_omni_validation_aware_diagnostic_pilot",
+ "research_positioning": "A research-engineering study that makes one public Xperience-10M sample episode inspectable, defines embodied-AI tasks over synchronized modalities, records baseline behavior, and uses the selected-episode Qwen3-Omni validation-aware diagnostic pilot as a verified but weak cross-episode baseline.",
"scope_boundary": {
"validated_episode_count": 1,
"aligned_frames": 5821,
@@ -13,7 +13,20 @@
"direction_extension_probe_count": 4,
"audio_featurized": true,
"raw_xperience10m_data_redistributed": false,
- "qwen3_omni_32_episode_claim": false
+ "qwen3_omni_32_episode_claim": false,
+ "qwen3_omni_verified_diagnostic_pilot": true,
+ "qwen3_omni_selected_episode_counts": {
+ "train": 96,
+ "val": 16,
+ "test": 16
+ },
+ "qwen3_omni_exported_window_counts": {
+ "train": 2848,
+ "val": 512,
+ "test": 448
+ },
+ "qwen3_omni_json_validity_rate": 0.875,
+ "qwen3_omni_validation_aware": true
},
"rows": [
{
@@ -36,92 +49,92 @@
],
"readout": "All 12 task contracts have committed metrics, predictions, and minimal baseline outputs."
},
- {
- "area": "Neural heads",
- "status": "verified",
- "evidence": [
- "scripts/neural_task_models.py",
- "results/episode_task_suite/neural_mlp/"
- ],
- "readout": "Each task also has a compact PyTorch MLP run over the same feature tensor and chronological split."
- },
- {
- "area": "Audio contribution study",
- "status": "verified",
- "evidence": [
- "scripts/audio_ablation_and_raw_upgrade.py",
- "results/audio_ablation/",
- "docs/data/audio_ablation_summary.json"
- ],
- "readout": "Audio variants improve the primary metric on 6 of 12 task contracts in this single-episode setting."
- },
- {
- "area": "Evaluation protocol",
- "status": "verified",
- "evidence": [
- "EVALUATION_PROTOCOL.md",
- "docs/data/evaluation_protocol.json",
- "scripts/build_evaluation_protocol.py"
- ],
- "readout": "Windowing, chronological split, per-task metrics, leakage controls, and current limitations are generated from committed metric artifacts."
- },
- {
- "area": "Research takeaways",
- "status": "verified",
- "evidence": [
- "RESEARCH_TAKEAWAYS.md",
- "docs/data/research_takeaways.json",
- "scripts/build_research_takeaways.py"
- ],
- "readout": "The main result interpretation is generated from committed metrics: chronological class shift, neural gains on dynamics/order/alignment, open retrieval/reconstruction problems, and the need for held-out episodes."
- },
- {
- "area": "Research roadmap",
- "status": "current",
- "evidence": [
- "RESEARCH_ROADMAP.md",
- "docs/data/research_roadmap.json"
- ],
- "readout": "The roadmap connects public-sample task development to 128-episode data preparation, Qwen3-Omni LoRA, foundation-model selection, robustness runs, world/policy branches, and the future Xperience-native pretraining goal."
- },
- {
- "area": "Foundation-model plan",
- "status": "current",
- "evidence": [
- "FOUNDATION_MODEL_PLAN.md",
- "docs/data/foundation_model_plan.json"
- ],
- "readout": "Qwen3-Omni remains the first trainable held-out LoRA baseline; Cosmos 3 is added as the first world-model/action-generation branch; OpenVLA/openpi/GR00T are policy candidates after action targets are explicit."
- },
- {
- "area": "Xperience Embodied Foundation Model",
- "status": "future_goal",
- "evidence": [
- "XPERIENCE_EMBODIED_FOUNDATION_MODEL_PRETRAINING.md"
- ],
- "readout": "A future full-corpus pretraining plan describes target modules, objectives, staged scale-up, hardware ranges, and evaluation for a domain-specific embodied foundation model."
- },
- {
- "area": "Official dataset wording",
- "status": "verified",
+ {
+ "area": "Neural heads",
+ "status": "verified",
+ "evidence": [
+ "scripts/neural_task_models.py",
+ "results/episode_task_suite/neural_mlp/"
+ ],
+ "readout": "Each task also has a compact PyTorch MLP run over the same feature tensor and chronological split."
+ },
+ {
+ "area": "Audio contribution study",
+ "status": "verified",
+ "evidence": [
+ "scripts/audio_ablation_and_raw_upgrade.py",
+ "results/audio_ablation/",
+ "docs/data/audio_ablation_summary.json"
+ ],
+ "readout": "Audio variants improve the primary metric on 6 of 12 task contracts in this single-episode setting."
+ },
+ {
+ "area": "Evaluation protocol",
+ "status": "verified",
+ "evidence": [
+ "EVALUATION_PROTOCOL.md",
+ "docs/data/evaluation_protocol.json",
+ "scripts/build_evaluation_protocol.py"
+ ],
+ "readout": "Windowing, chronological split, per-task metrics, leakage controls, and current limitations are generated from committed metric artifacts."
+ },
+ {
+ "area": "Research takeaways",
+ "status": "verified",
+ "evidence": [
+ "RESEARCH_TAKEAWAYS.md",
+ "docs/data/research_takeaways.json",
+ "scripts/build_research_takeaways.py"
+ ],
+ "readout": "The main result interpretation is generated from committed metrics: chronological class shift, neural gains on dynamics/order/alignment, open retrieval/reconstruction problems, and the need for held-out episodes."
+ },
+ {
+ "area": "Research roadmap",
+ "status": "current",
+ "evidence": [
+ "RESEARCH_ROADMAP.md",
+ "docs/data/research_roadmap.json"
+ ],
+ "readout": "The roadmap connects public-sample task development to the verified Qwen3-Omni diagnostic pilot, validation-aware diagnostics, foundation-model selection, robustness runs, world/policy branches, and the future Xperience-native pretraining goal."
+ },
+ {
+ "area": "Foundation-model plan",
+ "status": "current",
+ "evidence": [
+ "FOUNDATION_MODEL_PLAN.md",
+ "docs/data/foundation_model_plan.json"
+ ],
+ "readout": "Qwen3-Omni remains the first trainable held-out LoRA baseline; Cosmos 3 is added as the first world-model/action-generation branch; OpenVLA/openpi/GR00T are policy candidates after action targets are explicit."
+ },
+ {
+ "area": "Xperience Embodied Foundation Model",
+ "status": "future_goal",
+ "evidence": [
+ "XPERIENCE_EMBODIED_FOUNDATION_MODEL_PRETRAINING.md"
+ ],
+ "readout": "A future full-corpus pretraining plan describes target modules, objectives, staged scale-up, hardware ranges, and evaluation for a domain-specific embodied foundation model."
+ },
+ {
+ "area": "Official dataset wording",
+ "status": "verified",
"evidence": [
"XPERIENCE10M_DATASET_CARD_ALIGNMENT.md",
"docs/data/xperience10m_dataset_card_alignment.json"
],
- "readout": "Public wording is aligned to the official gated Xperience-10M dataset card, public sample card, and HF API metadata, including modalities, scale, access path, sample license/tooling, and current project coverage."
- },
- {
- "area": "Source alignment",
- "status": "verified",
- "evidence": [
- "SOURCE_ALIGNMENT_AUDIT.md",
- "docs/data/source_alignment_audit.json",
- "scripts/validate_source_alignment.py"
- ],
- "readout": "Source facts, sample details, API-listing notes, and project coverage are checked across repo docs, website, and HF cards."
- },
- {
- "area": "Website and HF mirrors",
+ "readout": "Public wording is aligned to the official gated Xperience-10M dataset card, public sample card, and HF API metadata, including modalities, scale, access path, sample license/tooling, and current project coverage."
+ },
+ {
+ "area": "Source alignment",
+ "status": "verified",
+ "evidence": [
+ "SOURCE_ALIGNMENT_AUDIT.md",
+ "docs/data/source_alignment_audit.json",
+ "scripts/validate_source_alignment.py"
+ ],
+ "readout": "Source facts, sample details, API-listing notes, and project coverage are checked across repo docs, website, and HF cards."
+ },
+ {
+ "area": "Website and HF mirrors",
"status": "verified",
"evidence": [
"docs/data/website_integrity.json",
@@ -152,12 +165,14 @@
},
{
"area": "Qwen3-Omni fine-tuning",
- "status": "data_preparation_full_metrics_pending",
+ "status": "verified_validation_aware_diagnostic_pilot_quality_target_not_met",
"evidence": [
- "results/omni_finetune/DATA_ACCESS_STATUS.md",
- "results/omni_finetune/MULTI_EPISODE_ACCESS_STATUS.md"
+ "docs/data/omni_finetune_verified_result.json",
+ "results/omni_finetune/verified_public/",
+ "scripts/omni/package_verified_omni_result.py",
+ "scripts/omni/audit_verified_omni_package.py"
],
- "readout": "The gated full dataset is available for a selected 128-episode pilot; final held-out metrics require completed preprocessing, manifest construction, training, and held-out evaluation."
+ "readout": "The selected 96/16/16 episode split produced a validation-aware public-safe held-out package with 3,808 exported windows, 512 validation windows, and 448 test predictions. JSON validity is 87.50%, below the 98% target, so it is a stronger diagnostic baseline but not a strong model-quality result."
},
{
"area": "Raw Xperience-10M redistribution",
@@ -171,21 +186,21 @@
],
"fast_research_route": [
"Read PROJECT_STATUS.md and EVIDENCE_CONTRACT.md to establish what is implemented.",
- "Open docs/data/project_packet.json for the machine-readable project path.",
- "Inspect RESEARCH_TAKEAWAYS.md and docs/data/research_takeaways.json before interpreting model scores.",
- "Inspect RESEARCH_ROADMAP.md and docs/data/research_roadmap.json for the path from public-sample task work to multi-episode modeling.",
- "Inspect FOUNDATION_MODEL_PLAN.md and docs/data/foundation_model_plan.json before choosing a backbone branch.",
- "Inspect XPERIENCE_EMBODIED_FOUNDATION_MODEL_PRETRAINING.md for the long-term full-corpus pretraining goal.",
- "Inspect docs/data/summary_metrics.json and results/episode_task_suite/neural_mlp/ to check the 12-task outputs.",
- "Inspect results/audio_ablation/AUDIO_ABLATION_SUMMARY.md before judging whether audio helps the current task suite.",
- "Inspect EVALUATION_PROTOCOL.md before judging task metrics or leakage controls.",
- "Inspect SOURCE_ALIGNMENT_AUDIT.md before judging source-card consistency across public surfaces.",
- "Inspect XPERIENCE10M_DATASET_CARD_ALIGNMENT.md before judging dataset wording.",
- "Inspect results/omni_finetune/DATA_ACCESS_STATUS.md before judging Qwen3-Omni scale-up status."
+ "Open docs/data/project_packet.json for the machine-readable project path.",
+ "Inspect RESEARCH_TAKEAWAYS.md and docs/data/research_takeaways.json before interpreting model scores.",
+ "Inspect RESEARCH_ROADMAP.md and docs/data/research_roadmap.json for the path from public-sample task work to multi-episode modeling.",
+ "Inspect FOUNDATION_MODEL_PLAN.md and docs/data/foundation_model_plan.json before choosing a backbone branch.",
+ "Inspect XPERIENCE_EMBODIED_FOUNDATION_MODEL_PRETRAINING.md for the long-term full-corpus pretraining goal.",
+ "Inspect docs/data/summary_metrics.json and results/episode_task_suite/neural_mlp/ to check the 12-task outputs.",
+ "Inspect results/audio_ablation/AUDIO_ABLATION_SUMMARY.md before judging whether audio helps the current task suite.",
+ "Inspect EVALUATION_PROTOCOL.md before judging task metrics or leakage controls.",
+ "Inspect SOURCE_ALIGNMENT_AUDIT.md before judging source-card consistency across public surfaces.",
+ "Inspect XPERIENCE10M_DATASET_CARD_ALIGNMENT.md before judging dataset wording.",
+ "Inspect docs/data/omni_finetune_verified_result.json before judging the Qwen3-Omni diagnostic pilot."
],
"current_reading_notes": [
- "Cross-episode generalization is evaluated in the later multi-episode stage.",
- "Older pilot path names refer to setup files, not completed held-out training results.",
+ "The validation-aware Qwen3-Omni diagnostic pilot is verified, but current held-out quality is still weak.",
+ "Use docs/data/omni_finetune_verified_result.json and the latest verified_public validation-aware package for current held-out results.",
"The current reconstruction task reconstructs feature vectors, not pixel-depth, mesh, NeRF, or Gaussian reconstruction.",
"Audio is one of the synchronized source modalities in the current task representation.",
"The audio ablation report compares audio/no-audio variants across all 12 task contracts in results/audio_ablation/.",
diff --git a/docs/data/research_roadmap.json b/docs/data/research_roadmap.json
index b3375062619fb68bd25699377fadf6554a00471a..f6d24b0562fb338919f5b0a2386214d199b8c46a 100644
--- a/docs/data/research_roadmap.json
+++ b/docs/data/research_roadmap.json
@@ -1,7 +1,7 @@
{
"title": "Ropedia Xperience-10M Research Roadmap",
- "summary": "Staged path from the public-sample task lab to multi-episode held-out evaluation, foundation-model selection, world/policy branches, and a future Xperience-native embodied foundation model.",
- "current_decision_point": "Keep the public-sample task suite as the development harness, prepare the selected official Xperience-10M episodes for the held-out Qwen3-Omni pilot, then branch into Cosmos 3 world modeling and policy-model experiments after the data preparation path is stable. The Xperience Embodied Foundation Model is a later full-corpus pretraining goal, not a current result.",
+ "summary": "Staged path from the public-sample task lab to a verified validation-aware Qwen3-Omni diagnostic pilot, structured-output improvement pass, foundation-model selection, world/policy branches, and a future Xperience-native embodied foundation model.",
+ "current_decision_point": "Keep the public-sample task suite as the development harness, use the verified selected-episode Qwen3-Omni validation-aware diagnostic pilot as the first cross-episode baseline, improve structured-output reliability and task-quality error analysis, then branch into Cosmos 3 world modeling and policy-model experiments after their targets are implemented. The Xperience Embodied Foundation Model is a later full-corpus pretraining goal, not a current result.",
"additional_development_directions": {
"source_document": "ADDITIONAL_DEVELOPMENT_DIRECTIONS.md",
"source_json": "docs/data/additional_development_directions.json",
@@ -33,8 +33,8 @@
},
{
"id": "multi_episode_data_staging",
- "name": "Multi-Episode Data Staging",
- "status": "active",
+ "name": "Multi-Episode Data Preparation",
+ "status": "implemented_for_first_pilot",
"entry_condition": "Gated dataset availability and enough storage for selected episodes.",
"deliverables": [
"128 selected episodes",
@@ -48,23 +48,26 @@
"results/omni_finetune/MULTI_EPISODE_ACCESS_STATUS.md",
"results/omni_finetune/source_discovery.json"
],
- "reader_takeaway": "The next scale decision is data preparation, with train/test separation at the episode level."
+ "reader_takeaway": "The first selected split is available for Qwen3-Omni diagnostics, with train/test separation at the episode level."
},
{
- "id": "qwen3_omni_lora_pilot",
- "name": "Qwen3-Omni LoRA Pilot",
- "status": "next",
+ "id": "qwen3_omni_lora_diagnostic_pilot",
+ "name": "Qwen3-Omni LoRA Validation-Aware Diagnostic Pilot",
+ "status": "verified_baseline",
"entry_condition": "Selected episodes are prepared locally with no train/test episode leakage.",
"deliverables": [
"dataset JSONL/media manifests",
"LoRA adapter checkpoint",
"progress logs",
+ "validation monitoring",
"held-out predictions",
"metrics",
"confusion matrices",
"run report"
],
"completion_evidence": [
+ "docs/data/omni_finetune_verified_result.json",
+ "results/omni_finetune/verified_public/",
"dataset_manifest.json",
"training_metadata.json",
"progress.jsonl",
@@ -72,7 +75,27 @@
"predictions.jsonl",
"RUN_REPORT.md"
],
- "reader_takeaway": "The first omni-model pilot should establish a complete held-out-episode training and evaluation loop."
+ "reader_takeaway": "The first omni-model pilot establishes the full held-out training/validation/evaluation loop, but the weak metrics make it a diagnostic baseline."
+ },
+ {
+ "id": "qwen3_omni_structured_output_error_analysis",
+ "name": "Structured-Output And Error-Analysis Pass",
+ "status": "active_next_step",
+ "entry_condition": "The validation-aware diagnostic package exists and shows weak held-out quality.",
+ "deliverables": [
+ "same 96/16/16 episode split",
+ "stricter JSON decoding or target formatting",
+ "episode/action/object error analysis",
+ "held-out test evaluation",
+ "comparison to the verified validation-aware baseline"
+ ],
+ "completion_evidence": [
+ "quality-target report",
+ "error-analysis tables",
+ "held-out metrics",
+ "verified public-safe package"
+ ],
+ "reader_takeaway": "The next pass should improve output reliability and task metrics before larger model-quality claims."
},
{
"id": "foundation_model_selection_matrix",
diff --git a/docs/data/research_roadmap_interactive.json b/docs/data/research_roadmap_interactive.json
index 23873eb030ec7e0292021b8c243444f8926f2928..3dcef156d8267f6e9c6c92de9b8f53cd785dc14b 100644
--- a/docs/data/research_roadmap_interactive.json
+++ b/docs/data/research_roadmap_interactive.json
@@ -127,7 +127,7 @@
"Build the episode taxonomy and data-quality diagnostics first.",
"Lock the benchmark protocol and split manifests before reporting model scores.",
"Add representation-learning and skill-graph objectives once enough episodes are staged.",
- "Add affordance, 3D/4D memory, and policy-retargeting branches after labels and action targets are auditable."
+ "Add affordance, 3D/4D memory, and policy-retargeting branches after labels and action targets are measurable."
],
"public_boundary": "These are proposed development tracks. They are not reported as completed held-out benchmark results.",
"source_document": "ADDITIONAL_DEVELOPMENT_DIRECTIONS.md",
@@ -2035,7 +2035,7 @@
"step": 1
},
{
- "action": "Run Qwen3-Omni LoRA to establish the full train/eval loop.",
+ "action": "Run validation-aware Qwen3-Omni LoRA to improve the verified diagnostic baseline.",
"name": "First held-out baseline",
"step": 2
},
@@ -2222,7 +2222,7 @@
],
"status": "planning_artifact"
},
- "generated_at_utc": "2026-06-04T21:22:15+00:00",
+ "generated_at_utc": "2026-06-06T13:49:32+00:00",
"omni_plan": {
"adapter": "LoRA rank 16, alpha 32, dropout 0.05",
"backbone": "Qwen/Qwen3-Omni-30B-A3B-Instruct",
@@ -2279,13 +2279,15 @@
],
"entry_condition": "Gated dataset availability and enough storage for selected episodes.",
"id": "multi_episode_data_staging",
- "name": "Multi-Episode Data Staging",
- "reader_takeaway": "The next scale decision is data preparation, with train/test separation at the episode level.",
- "stage": "scale_up",
- "status": "active"
+ "name": "Multi-Episode Data Preparation",
+ "reader_takeaway": "The first selected split is available for Qwen3-Omni diagnostics, with train/test separation at the episode level.",
+ "stage": "future",
+ "status": "implemented_for_first_pilot"
},
{
"completion_evidence": [
+ "docs/data/omni_finetune_verified_result.json",
+ "results/omni_finetune/verified_public/",
"dataset_manifest.json",
"training_metadata.json",
"progress.jsonl",
@@ -2297,17 +2299,39 @@
"dataset JSONL/media manifests",
"LoRA adapter checkpoint",
"progress logs",
+ "validation monitoring",
"held-out predictions",
"metrics",
"confusion matrices",
"run report"
],
"entry_condition": "Selected episodes are prepared locally with no train/test episode leakage.",
- "id": "qwen3_omni_lora_pilot",
- "name": "Qwen3-Omni LoRA Pilot",
- "reader_takeaway": "The first omni-model pilot should establish a complete held-out-episode training and evaluation loop.",
- "stage": "omni",
- "status": "next"
+ "id": "qwen3_omni_lora_diagnostic_pilot",
+ "name": "Qwen3-Omni LoRA Validation-Aware Diagnostic Pilot",
+ "reader_takeaway": "The first omni-model pilot establishes the full held-out training/validation/evaluation loop, but the weak metrics make it a diagnostic baseline.",
+ "stage": "future",
+ "status": "verified_baseline"
+ },
+ {
+ "completion_evidence": [
+ "quality-target report",
+ "error-analysis tables",
+ "held-out metrics",
+ "verified public-safe package"
+ ],
+ "deliverables": [
+ "same 96/16/16 episode split",
+ "stricter JSON decoding or target formatting",
+ "episode/action/object error analysis",
+ "held-out test evaluation",
+ "comparison to the verified validation-aware baseline"
+ ],
+ "entry_condition": "The validation-aware diagnostic package exists and shows weak held-out quality.",
+ "id": "qwen3_omni_structured_output_error_analysis",
+ "name": "Structured-Output And Error-Analysis Pass",
+ "reader_takeaway": "The next pass should improve output reliability and task metrics before larger model-quality claims.",
+ "stage": "future",
+ "status": "active_next_step"
},
{
"completion_evidence": [
@@ -2404,7 +2428,7 @@
"visualization.rrd"
],
"selection_strategy": "stratified_round_robin_by_top_level_session",
- "status": "selected_episode_preparation",
+ "status": "verified_validation_aware_diagnostic_pilot",
"target_episodes": 128,
"valid_candidates": 12102
},
diff --git a/docs/data/research_takeaways.json b/docs/data/research_takeaways.json
index f6ef5f09db3ad85c43dc90f371628818ff7f5adc..22a55a21160b0cb4c79c99bdfa6c7aa26a4a40b6 100644
--- a/docs/data/research_takeaways.json
+++ b/docs/data/research_takeaways.json
@@ -1,7 +1,7 @@
{
"title": "Ropedia Xperience-10M Research Takeaways",
"status": "pass",
- "generated_at_utc": "2026-06-04T16:42:13+00:00",
+ "generated_at_utc": "2026-06-06T13:49:32+00:00",
"source_files": [
"docs/data/summary_metrics.json",
"results/episode_task_suite/summary_report.json",
@@ -166,23 +166,27 @@
{
"id": "scale_requires_episodes",
"title": "The next scientific unit is held-out episodes, not more adjacent windows",
- "readout": "The prepared Qwen3-Omni path now targets a selected 128-episode pilot; held-out metrics will be reported after staging, training, and evaluation complete.",
+ "readout": "The selected Qwen3-Omni path now has a verified validation-aware held-out diagnostic pilot. It proves the cross-episode train/validation/eval loop, but the weak metrics show that structured-output reliability and task-quality error analysis are the next modeling problems.",
"evidence": [
{
- "label": "target_episodes",
+ "label": "selected_episodes",
"value": 128
},
{
- "label": "selected_sessions",
- "value": 128
+ "label": "held_out_test_windows",
+ "value": 448
+ },
+ {
+ "label": "json_validity_rate",
+ "value": 0.875
},
{
- "label": "valid_candidates",
- "value": 12102
+ "label": "action_macro_f1",
+ "value": 0.0026621494447581404
}
],
- "source": "results/omni_finetune/MULTI_EPISODE_ACCESS_STATUS.md",
- "current_scope": "The selected-episode Qwen3-Omni fine-tune requires completed data preparation and held-out evaluation; the 32-episode Qwen3-Omni fine-tune requires gated data preparation before any real held-out metric is reported."
+ "source": "docs/data/omni_finetune_verified_result.json",
+ "current_scope": "The selected-episode Qwen3-Omni validation-aware diagnostic pilot is verified, but held-out quality is still weak and JSON validity remains below the 98% target."
}
]
}
diff --git a/results/omni_finetune/verified_public/xperience10m_qwen3_omni_128ep_96train_16val_16test_valmon_20260605_eval/PUBLIC_RESULT_SUMMARY.md b/results/omni_finetune/verified_public/xperience10m_qwen3_omni_128ep_96train_16val_16test_valmon_20260605_eval/PUBLIC_RESULT_SUMMARY.md
new file mode 100644
index 0000000000000000000000000000000000000000..c8c80139ad777d11953b4f95f8e69dc7d10b9fee
--- /dev/null
+++ b/results/omni_finetune/verified_public/xperience10m_qwen3_omni_128ep_96train_16val_16test_valmon_20260605_eval/PUBLIC_RESULT_SUMMARY.md
@@ -0,0 +1,25 @@
+# Verified Omni Fine-Tuning Result
+
+- Backbone: `qwen3_omni_lora`
+- Dataset run: `xperience10m_qwen3_omni_128ep_96train_16val_16test_valmon_20260605`
+- Training run: `xperience10m_qwen3_omni_128ep_96train_16val_16test_valmon_20260605_lora`
+- Evaluation run: `xperience10m_qwen3_omni_128ep_96train_16val_16test_valmon_20260605_eval`
+- Validation status: `verified`
+- Held-out eval split: `test`
+- Held-out episodes: `14`
+- Prediction rows: `448`
+
+## Primary Metrics
+
+- json_validity_rate: `0.875`
+- action_macro_f1: `0.0026621494447581404`
+- subtask_accuracy: `0.006696428571428571`
+- transition_accuracy: `0.8504464285714286`
+- next_action_accuracy: `0.024553571428571428`
+- contact_accuracy: `0.6450892857142857`
+- object_micro_f1: `0.22299431459254582`
+- held_out_episode_count: `14`
+
+Raw Xperience-10M files, base-model weights, adapter or checkpoint weights, full checkpoints, and large archives are not included.
+
+Use this package as the source for README, website, and Hugging Face updates.
diff --git a/results/omni_finetune/verified_public/xperience10m_qwen3_omni_128ep_96train_16val_16test_valmon_20260605_eval/dataset/dataset_manifest.json b/results/omni_finetune/verified_public/xperience10m_qwen3_omni_128ep_96train_16val_16test_valmon_20260605_eval/dataset/dataset_manifest.json
new file mode 100644
index 0000000000000000000000000000000000000000..5e29f896c1d498985c16b0945ddd0e50e226d7d3
--- /dev/null
+++ b/results/omni_finetune/verified_public/xperience10m_qwen3_omni_128ep_96train_16val_16test_valmon_20260605_eval/dataset/dataset_manifest.json
@@ -0,0 +1,9694 @@
+{
+ "run_id": "xperience10m_qwen3_omni_128ep_96train_16val_16test_valmon_20260605_dataset",
+ "dataset_path": "/results/omni_finetune/xperience10m_qwen3_omni_128ep_96train_16val_16test_valmon_20260605_dataset/dataset.jsonl",
+ "num_samples": 3808,
+ "num_episodes": 119,
+ "split_counts": {
+ "train": 2848,
+ "val": 512,
+ "test": 448
+ },
+ "label_counts": {
+ "Fold paper strip": 84,
+ "Cut cardboard": 76,
+ "Manipulate paper strip": 51,
+ "Cut cardboard shape": 50,
+ "Draw line on cardboard": 29,
+ "Cut cardboard piece": 27,
+ "Place cardboard piece": 23,
+ "Pick up phone": 23,
+ "Sort beads": 23,
+ "Hold smartphone": 19,
+ "Pick up scissors": 18,
+ "Manipulate paper star": 18,
+ "Mark cardboard with pen": 17,
+ "Use phone": 17,
+ "Fold paper strip into lucky star": 17,
+ "Observe workspace": 17,
+ "Sort beads by color": 16,
+ "Reach for beads": 16,
+ "Pick up smartphone": 15,
+ "Mark cardboard piece": 15,
+ "Mark cardboard": 14,
+ "Trim cardboard piece": 14,
+ "Fold paper strip into star": 14,
+ "Place product on shelf": 13,
+ "Cut cardboard with scissors": 13,
+ "Pick up product": 12,
+ "Release paper strip": 12,
+ "Place item on shelf": 12,
+ "Hold paper strip": 12,
+ "Pick up container": 12,
+ "Cut cardboard square": 12,
+ "Pick up product from box": 11,
+ "Place can on shelf": 11,
+ "Place phone on desk": 11,
+ "Fold cardboard": 11,
+ "Use smartphone": 11,
+ "Move phone": 11,
+ "Reposition ruler": 11,
+ "Type on smartphone": 10,
+ "Draw lines on cardboard": 10,
+ "Pick up paper strip": 10,
+ "Fold paper star": 10,
+ "Hold product": 9,
+ "Cut cardboard with utility knife": 9,
+ "Approach desk": 9,
+ "Pick up utility knife": 9,
+ "Adjust item on shelf": 9,
+ "Adjust ruler position": 9,
+ "Approach workstation": 9,
+ "Cut cardboard triangle": 9,
+ "Cut cardboard strip": 9,
+ "Hold cardboard piece": 9,
+ "Marking cardboard piece": 9,
+ "Hold ruler and mark cardboard": 9,
+ "Grasp paper strip": 9,
+ "Place down scissors": 8,
+ "Continue folding paper strip": 8,
+ "Fold paper strip into knot": 8,
+ "Inflate paper star": 8,
+ "Pick up canned food": 8,
+ "Move towards shelf": 8,
+ "Move ruler": 8,
+ "Mark cardboard with marker": 8,
+ "Inspect jar": 8,
+ "Organize cardboard pieces": 8,
+ "Interact with smartphone": 8,
+ "Place scissors on table": 8,
+ "Arrange buttons": 8,
+ "Write on paper": 8,
+ "Write on notepad": 8,
+ "Writing on notepad": 8,
+ "Mark line on cardboard": 7,
+ "Reach for phone": 7,
+ "Walk towards table": 7,
+ "Place canned food on shelf": 7,
+ "Cut along the marked line": 7,
+ "Pick up can": 7,
+ "Adjust puzzle piece": 7,
+ "Carry cardboard piece": 7,
+ "Fold cardboard shape": 7,
+ "Arrange Mahjong tiles": 7,
+ "Cut newspaper": 7,
+ "Finish wiping and inspect jar": 7,
+ "Hold items and inspect shelf": 7,
+ "Hold and mark cardboard piece": 7,
+ "Move away from workstation": 6,
+ "Remove ruler": 6,
+ "Reach for product": 6,
+ "Pick up pen": 6,
+ "Holding marker": 6,
+ "Pick up cardboard": 6,
+ "Pick up cardboard strip": 6,
+ "Pick up new cardboard piece": 6,
+ "Place puzzle piece": 6,
+ "Manipulate puzzle pieces": 6,
+ "Mark lines on cardboard": 6,
+ "Release cardboard shape": 6,
+ "Hold water bottle": 6,
+ "Hold phone": 6,
+ "Rearrange Mahjong tiles": 6,
+ "Release cardboard": 6,
+ "Browsing smartphone content": 6,
+ "Wipe the plastic jar": 6,
+ "Pick up tin can": 6,
+ "Pick up stapler": 6,
+ "Draw grid line with pen": 6,
+ "Draw grid line": 6,
+ "Sort buttons": 6,
+ "Reach for next item": 5,
+ "Reach into box": 5,
+ "Move product to shelf": 5,
+ "Reach for button": 5,
+ "Release scissors": 5,
+ "Interact with phone": 5,
+ "Place phone down": 5,
+ "Separate cardboard piece": 5,
+ "Move smartphone": 5,
+ "Hold ruler on cardboard": 5,
+ "Reposition hands": 5,
+ "Move along shelf": 5,
+ "Hold ruler": 5,
+ "Cut cardboard piece with scissors": 5,
+ "Position cardboard piece": 5,
+ "Place marker down": 5,
+ "Sort paper star": 5,
+ "Sort paper stars": 5,
+ "Release puzzle piece": 5,
+ "Search for puzzle piece": 5,
+ "Hold beads": 5,
+ "Move along the shelves": 5,
+ "Manipulate small component": 5,
+ "Manipulate component on strip": 5,
+ "Place strip on table": 5,
+ "Manipulate component": 5,
+ "Align canned goods on shelf": 5,
+ "Drawing grid line with ruler": 5,
+ "Drawing grid line with pen and ruler": 5,
+ "Drawing grid line": 5,
+ "Fold lucky star": 5,
+ "Sort colored tiles": 5,
+ "Pick up colored tile": 5,
+ "Place colored tile": 5,
+ "Sort tiles": 5,
+ "Sort tiles by color": 5,
+ "Initiate star folding": 5,
+ "Manipulate paper decoration": 5,
+ "Manipulate paper edge": 5,
+ "Manipulate adhesive strip": 5,
+ "Place jar on shelf": 4,
+ "Place product in box": 4,
+ "Pick up button": 4,
+ "Place button": 4,
+ "Put down scissors": 4,
+ "Mark cardboard with pen and ruler": 4,
+ "Hold cardboard pieces": 4,
+ "Hold portable charger": 4,
+ "Fold purple paper strip": 4,
+ "Fold purple paper": 4,
+ "Hold and crease purple paper": 4,
+ "Release paper": 4,
+ "Retrieve paper strips": 4,
+ "Fold and organize paper strips": 4,
+ "Reach into bag": 4,
+ "Position hands for work": 4,
+ "Manipulate quilling strip": 4,
+ "Begin rolling quilling strip": 4,
+ "Examine item": 4,
+ "Move cardboard box": 4,
+ "Walk towards aisle": 4,
+ "Hold cardboard": 4,
+ "Walk through workspace": 4,
+ "Manipulate quilled paper strip": 4,
+ "Cut cardboard tube": 4,
+ "Cut cardboard into triangles": 4,
+ "Reach for container": 4,
+ "Move container toward shelf": 4,
+ "Move away from shelf": 4,
+ "Pick up marker": 4,
+ "Walk away": 4,
+ "Pick up paper star": 4,
+ "Marking lines on cardboard": 4,
+ "Adjusting a puzzle piece": 4,
+ "Draw line along ruler": 4,
+ "Cap marker": 4,
+ "Manipulate craft piece": 4,
+ "Manipulate craft paper strips": 4,
+ "Operate smartphone": 4,
+ "Pick up item from shelf": 4,
+ "Sort star-shaped beads": 4,
+ "Sort beads on table": 4,
+ "Hold instructional sign": 4,
+ "Pick up star-shaped bead": 4,
+ "Place bead on table": 4,
+ "Draw lines with ruler": 4,
+ "Grasp origami stars": 4,
+ "Place water bottle on table": 4,
+ "Vacuum the carpet": 4,
+ "Push vacuum cleaner": 4,
+ "Adjust vacuum cleaner position": 4,
+ "Vacuum edge of carpet": 4,
+ "Move vacuum cleaner": 4,
+ "Place finished star on table": 4,
+ "Adjust Mahjong tiles": 4,
+ "Reach for Mahjong tiles": 4,
+ "Rearrange Mahjong tile": 4,
+ "Adjust Mahjong tile": 4,
+ "Align Mahjong tiles": 4,
+ "Move Mahjong tile": 4,
+ "Fold ribbon": 4,
+ "Hold small piece of ribbon": 4,
+ "Manipulate ribbon piece": 4,
+ "Fold and manipulate ribbon": 4,
+ "Manipulate ribbon knot": 4,
+ "Continue cutting newspaper": 4,
+ "Adjust tile row alignment": 4,
+ "Adjust Mahjong tile alignment": 4,
+ "Adjust Mahjong tile on the stack": 4,
+ "Measure and mark cardboard": 4,
+ "Cut cardboard strip with scissors": 4,
+ "Scroll on smartphone": 4,
+ "Align ruler and mark cardboard": 4,
+ "Assemble cardboard pieces": 4,
+ "Arrange cardboard piece": 4,
+ "Cut along the line": 4,
+ "Place cardboard piece on stack": 4,
+ "Arrange buttons on the table": 4,
+ "Move hand over button pile": 4,
+ "Arrange orange buttons": 4,
+ "Move pen away": 4,
+ "Gathering star beads": 4,
+ "Manipulate paper stars": 4,
+ "Adjust cardboard": 4,
+ "Set down scissors and pick up power bank": 4,
+ "Reposition cardboard for cutting": 4,
+ "Arrange cardboard pieces": 4,
+ "Mark cardboard strip with pen": 4,
+ "Pick up pink water bottle": 4,
+ "Place down pink water bottle": 4,
+ "Place star in row": 4,
+ "Pick up star": 4,
+ "Begin folding paper strip": 4,
+ "Fold paper strip into a star": 4,
+ "Manipulate folded paper star": 4,
+ "Reaching for beads": 4,
+ "Place cardboard square": 4,
+ "Arrange buttons in a line": 4,
+ "Approaching and pressing the door switch": 4,
+ "Bend and manipulate plastic strip": 4,
+ "Pick up and sort cardboard": 4,
+ "Move camera over surface": 4,
+ "Observe sorting progress": 4,
+ "Lock phone": 4,
+ "Reach for cardboard box": 4,
+ "Reach for object": 4,
+ "Move to desk": 4,
+ "Gathering items": 4,
+ "Place items on table": 4,
+ "Gathering colored beads": 4,
+ "Arrange beads by color": 4,
+ "Sort star-shaped objects by color": 4,
+ "Sort star-shaped objects": 4,
+ "Sort yellow star-shaped objects": 4,
+ "Sort purple star-shaped objects": 4,
+ "View phone screen": 4,
+ "Viewing phone screen": 4,
+ "Placing phone down": 4,
+ "Place button in group": 4,
+ "Move away from table": 4,
+ "Placing paper strip": 4,
+ "Securing paper structure": 4,
+ "Secure paper edges with adhesive": 4,
+ "Pick up product from bin": 3,
+ "Reach for next product": 3,
+ "Arrange canned products on shelf": 3,
+ "Move bin to shelf area": 3,
+ "Hold item and adjust posture": 3,
+ "Grasp product from box": 3,
+ "Grasp product from shelf": 3,
+ "Move product to box": 3,
+ "Manipulate cardboard piece": 3,
+ "Position ruler on cardboard": 3,
+ "Stack cardboard pieces": 3,
+ "Place cardboard": 3,
+ "Place down paper pieces": 3,
+ "Release folded paper": 3,
+ "Release quilling strip": 3,
+ "Inspect cardboard piece": 3,
+ "Reposition hand": 3,
+ "Touch shelf edge": 3,
+ "Release label": 3,
+ "Remove shelf label": 3,
+ "Carry stool to next shelf": 3,
+ "Place stool on floor": 3,
+ "Observe shelf": 3,
+ "Adjust hand position": 3,
+ "Arrange star-shaped beads": 3,
+ "Move pen": 3,
+ "Move towards table": 3,
+ "Observe room": 3,
+ "Check watch": 3,
+ "Manipulate and inspect colorful pieces": 3,
+ "Manipulate colorful pieces": 3,
+ "Hold power bank and cable": 3,
+ "Interact with colleagues": 3,
+ "Hold small white box": 3,
+ "Adjust smartphone and sort pieces": 3,
+ "Pick up cardboard piece": 3,
+ "Release cardboard piece": 3,
+ "Walk across office": 3,
+ "Pick up cardboard cutout": 3,
+ "Walk with cardboard cutout": 3,
+ "Finish placing cardboard cutouts": 3,
+ "Organize tools and materials": 3,
+ "Move cardboard piece": 3,
+ "Hold cardboard strip": 3,
+ "Reposition scissors": 3,
+ "Move away from desk": 3,
+ "Move to shelf": 3,
+ "Move marker and adjust hand": 3,
+ "Identify next cardboard piece": 3,
+ "Reach for can": 3,
+ "Open door": 3,
+ "Hold craft tool": 3,
+ "Approach table": 3,
+ "Arrange paper strips": 3,
+ "Hold and bend paper strip": 3,
+ "Scan for next piece": 3,
+ "Positioning puzzle piece": 3,
+ "Move puzzle piece": 3,
+ "Adjusting puzzle piece": 3,
+ "Hold ruler and pen steady": 3,
+ "Moving ruler": 3,
+ "Approach packing area": 3,
+ "Deposit beads into box": 3,
+ "Combine bead piles": 3,
+ "Cut light green fabric": 3,
+ "Continue cutting fabric": 3,
+ "Cut fabric with scissors": 3,
+ "Adjusting fabric for cutting": 3,
+ "Adjusting fabric position": 3,
+ "Cutting fabric": 3,
+ "Mark fabric with pen": 3,
+ "Mark fabric": 3,
+ "Manipulate cardboard shape": 3,
+ "Hold small cardboard pieces": 3,
+ "sort craft materials": 3,
+ "Release smartphone": 3,
+ "Sort small craft pieces": 3,
+ "Move product towards shelf": 3,
+ "Move to box": 3,
+ "Place container on shelf": 3,
+ "Place item in shopping bag": 3,
+ "Sort beads on the table": 3,
+ "Reposition ruler and pen": 3,
+ "Reposition pen and prepare for next line": 3,
+ "Place pen on cardboard": 3,
+ "Draw straight lines on cardboard": 3,
+ "Sort origami stars": 3,
+ "Walk in hallway": 3,
+ "Reach for stars": 3,
+ "Walk towards desk": 3,
+ "Sort light blue origami stars": 3,
+ "Sort origami stars by color": 3,
+ "Move origami stars": 3,
+ "Hold and view phone": 3,
+ "Cut cardboard pieces with scissors": 3,
+ "Vacuuming carpet edge": 3,
+ "Vacuuming carpet corner": 3,
+ "Vacuuming the carpet edge": 3,
+ "Vacuuming along the wall edge": 3,
+ "Hold product package": 3,
+ "Check phone": 3,
+ "Hold charging cable": 3,
+ "Hold items in hand": 3,
+ "Hold and examine item": 3,
+ "Pick up pack from shelf": 3,
+ "fold purple ribbon": 3,
+ "Position ribbon piece": 3,
+ "Place ribbon onto project": 3,
+ "Secure ribbon with needle": 3,
+ "Reach for shelf": 3,
+ "Place smartphone on desk": 3,
+ "Reach for water bottle": 3,
+ "Hold scissors": 3,
+ "Move scissors away": 3,
+ "Place scissors down": 3,
+ "Arrange tiles into row": 3,
+ "Pick up Mahjong tile": 3,
+ "Place Mahjong tile on the stack": 3,
+ "Place Mahjong tile on stack": 3,
+ "Hold ruler and draw line": 3,
+ "Draw line": 3,
+ "Hold ruler and marker": 3,
+ "Tap smartphone screen": 3,
+ "Scroll through photo gallery": 3,
+ "Typing message on smartphone": 3,
+ "Typing on smartphone": 3,
+ "Tapping smartphone screen": 3,
+ "Tapping on smartphone screen": 3,
+ "Putting away smartphone": 3,
+ "Stop measuring and put down tools": 3,
+ "Positioning ruler on cardboard": 3,
+ "Draw line with pen": 3,
+ "Prepare to draw lines": 3,
+ "Remove ruler and marker": 3,
+ "Walking through classroom": 3,
+ "Move marker away": 3,
+ "Position ruler and mark cardboard": 3,
+ "Mark cardboard with ruler": 3,
+ "Reposition utility knife": 3,
+ "Tear off cardboard segment": 3,
+ "Reach for craft items": 3,
+ "Sort craft items": 3,
+ "Place hand on table": 3,
+ "Browse smartphone screen": 3,
+ "Scroll smartphone screen": 3,
+ "Put down smartphone": 3,
+ "Place smartphone down": 3,
+ "Adjust container on shelf": 3,
+ "Adjust cans in container": 3,
+ "Adjust cans in tray": 3,
+ "Adjusting canned goods on shelf": 3,
+ "Sorting buttons": 3,
+ "Sort orange buttons": 3,
+ "Sort orange button": 3,
+ "Move orange buttons": 3,
+ "Sort purple beads": 3,
+ "Sort beads by hand": 3,
+ "Count and record paper stars": 3,
+ "Connect cable to device": 3,
+ "Count and arrange paper stars": 3,
+ "Count paper stars": 3,
+ "Pick up puzzle piece": 3,
+ "Place piece into puzzle": 3,
+ "Manipulate puzzle piece": 3,
+ "Observe puzzle progress": 3,
+ "Attempt to fit puzzle piece": 3,
+ "Hold tray of canned goods": 3,
+ "Position tray": 3,
+ "Carry crate of cans": 3,
+ "Place crate on floor": 3,
+ "Wipe item": 3,
+ "Place item back": 3,
+ "Reach for retail item": 3,
+ "Grasp retail item": 3,
+ "Adjust retail items on shelf": 3,
+ "Pick up retail item": 3,
+ "Align and place retail item": 3,
+ "Arrange items on shelf": 3,
+ "Adjust retail item position": 3,
+ "Reach for star": 3,
+ "Retrieve star": 3,
+ "Cut cardboard grid": 3,
+ "Prepare paper strip": 3,
+ "Place star on table": 3,
+ "Place phone on table": 3,
+ "Cut along the edge of the newspaper": 3,
+ "Cut along the newspaper edge": 3,
+ "Browsing mobile phone": 3,
+ "Browse mobile phone": 3,
+ "Cut newspaper with scissors": 3,
+ "Gather pieces": 3,
+ "Move pieces into box": 3,
+ "Gather pieces into box": 3,
+ "Scrolling or navigating on phone": 3,
+ "Scrolling and viewing content on phone": 3,
+ "Sort and arrange buttons": 3,
+ "Sort button": 3,
+ "Sort and adjust button line": 3,
+ "Sort and place buttons": 3,
+ "Walking in the hallway": 3,
+ "Entering the VR training room": 3,
+ "Greeting/acknowledging participants": 3,
+ "Move through the training room": 3,
+ "Manipulate plastic strips": 3,
+ "Manipulate plastic strip": 3,
+ "Hold and bend plastic strip": 3,
+ "Fold plastic strip": 3,
+ "Pick up charging cable": 3,
+ "Hold electronic item": 3,
+ "Pick up electronic item": 3,
+ "Inspect electronic item": 3,
+ "Inspect smartphone box": 3,
+ "Hold smartphone box": 3,
+ "Examine product": 3,
+ "Move plastic storage bin": 3,
+ "Hold container of canned food": 3,
+ "Move towards aisle": 3,
+ "Approach restocking supplies": 3,
+ "Move pineapple chips": 3,
+ "Sort and arrange cardboard pieces": 3,
+ "Reach for cardboard piece": 3,
+ "Sort and stack cardboard pieces": 3,
+ "Walking towards workstation": 3,
+ "Sort small objects": 3,
+ "Sort buttons by color": 3,
+ "Sort button by color": 3,
+ "Reach for item in box": 2,
+ "Pick up nut bar box": 2,
+ "Place canned product on shelf": 2,
+ "Pick up canned product": 2,
+ "Reach for next canned product": 2,
+ "Pick up plastic bin": 2,
+ "Retract hand": 2,
+ "Hold and wipe product": 2,
+ "Wipe down shelf": 2,
+ "Wipe product": 2,
+ "Place jar in box": 2,
+ "Wipe shelf": 2,
+ "Pick up pickle jar": 2,
+ "Hold pickle jar": 2,
+ "Hold cleaning cloth": 2,
+ "Pick up product from shelf": 2,
+ "Move to next section": 2,
+ "Prepare to place product": 2,
+ "Grasp next item": 2,
+ "Position scissors to cut cardboard": 2,
+ "Position scissors": 2,
+ "Walk through corridor": 2,
+ "Arrive at a different workstation": 2,
+ "Move vacuum cleaner hose": 2,
+ "Mark cardboard with ruler and pen": 2,
+ "Hold ruler steady": 2,
+ "Move marker and ruler": 2,
+ "Align ruler on cardboard": 2,
+ "Plug cable into portable charger": 2,
+ "Pick up portable charger": 2,
+ "Place charger on table": 2,
+ "Hold charger and cable": 2,
+ "Manipulate power cable plug": 2,
+ "Insert plug into power adapter": 2,
+ "Hold power adapter": 2,
+ "Align charging cable": 2,
+ "Insert charging cable": 2,
+ "Observe desktop layout": 2,
+ "Pick up yellow paper strip": 2,
+ "Adjust paper strip": 2,
+ "Hold charger": 2,
+ "Open small case": 2,
+ "Measure cardboard with ruler": 2,
+ "Pick up yellow item": 2,
+ "Hold blue product box": 2,
+ "Wipe shelf surface": 2,
+ "Inspect product": 2,
+ "Clean shelf": 2,
+ "Place ketchup bottle on shelf": 2,
+ "Draw line with marker": 2,
+ "Draw straight line": 2,
+ "Mark straight line": 2,
+ "Move ruler and tools": 2,
+ "Pick up small cardboard piece": 2,
+ "Cut cardboard along line": 2,
+ "Align ruler with crease": 2,
+ "Cut cardboard strip with utility knife": 2,
+ "Hold container lid": 2,
+ "Closing the door": 2,
+ "Grasp cleaning bottle": 2,
+ "Grasping cleaning cloth": 2,
+ "Adjust pot position": 2,
+ "Start cutting": 2,
+ "Organize products": 2,
+ "Close cardboard box": 2,
+ "Grasp package": 2,
+ "Observe shelf status": 2,
+ "Inspect product lid": 2,
+ "Reach for another item": 2,
+ "Discard item into bin": 2,
+ "Walk towards next aisle": 2,
+ "Reach for product labels": 2,
+ "Hold product labels": 2,
+ "Examine labels": 2,
+ "Pick up bottled sauce": 2,
+ "Pick up supplement bottle": 2,
+ "Hold supplement bottle": 2,
+ "Open supplement bottle": 2,
+ "Pick up item": 2,
+ "Place item in container": 2,
+ "Pick up another item": 2,
+ "Adjust grip on container": 2,
+ "Pick up oil bottle": 2,
+ "Inspect supplement bottle": 2,
+ "Pick up spice jar": 2,
+ "Stand up and walk away": 2,
+ "Prepare to sort beads": 2,
+ "Align ruler": 2,
+ "Adjust grip": 2,
+ "Drawing lines on cardboard": 2,
+ "Reposition marker": 2,
+ "Mark lines with marker": 2,
+ "Position the ruler": 2,
+ "Insert charging cable into power bank": 2,
+ "Sort colorful pieces": 2,
+ "Touch pieces in box": 2,
+ "Place white box on table": 2,
+ "Sort small colorful pieces": 2,
+ "Sorting colorful paper pieces": 2,
+ "Manipulate paper piece": 2,
+ "Use phone to check instructions": 2,
+ "Trace pattern on cardboard": 2,
+ "Remove cardboard pattern": 2,
+ "Remove cardboard pattern piece": 2,
+ "Cut out cardboard pattern": 2,
+ "Cut cardboard pattern": 2,
+ "Adjust cardboard position": 2,
+ "Interact with smartphone screen": 2,
+ "Pick up metal ruler": 2,
+ "Move pen aside": 2,
+ "Reposition and cut": 2,
+ "Hold quilling paper": 2,
+ "Hold quilled paper coil": 2,
+ "Manipulate small paper segment": 2,
+ "Place down paper segment": 2,
+ "Browse and interact with phone interface": 2,
+ "Interacting with phone screen": 2,
+ "Pick up light blue strip": 2,
+ "Inspect strip": 2,
+ "Manipulate light blue strip": 2,
+ "Place scissors aside": 2,
+ "Stacking cardboard pieces": 2,
+ "Moving hand towards cardboard stack": 2,
+ "Moving hand": 2,
+ "Position cardboard for cutting": 2,
+ "Put down water bottle": 2,
+ "Placing piece on stack": 2,
+ "Reach for and pick up smartphone": 2,
+ "Pick up item from bin": 2,
+ "Pick up next item from bin": 2,
+ "Hold item": 2,
+ "Inspect and place item on shelf": 2,
+ "Check smart watch": 2,
+ "Withdraw hand": 2,
+ "Pick up jar": 2,
+ "Pick up sauce bottle": 2,
+ "Hold empty container": 2,
+ "Assess shelf arrangement": 2,
+ "Observe and walk through store": 2,
+ "Inspect shelf condition": 2,
+ "Observe colleague and workspace": 2,
+ "Walk towards shelves": 2,
+ "Approach boxes": 2,
+ "Extract wire hangers from box": 2,
+ "Bundle display hooks": 2,
+ "Move through aisle": 2,
+ "Pick up items from the shopping bag": 2,
+ "Place items on the shelf": 2,
+ "Place marked piece down": 2,
+ "Release cardboard piece and gesture": 2,
+ "Observe and pause": 2,
+ "Gesturing": 2,
+ "Resume observation": 2,
+ "Place cans into box": 2,
+ "Arrange cans in box": 2,
+ "Arrange cans on shelf": 2,
+ "Adjust position": 2,
+ "Place container in bin": 2,
+ "Adjust cans in bin": 2,
+ "Hold and inspect can": 2,
+ "Adjust perspective": 2,
+ "Inspect shelf and organize stock": 2,
+ "Picking up stock": 2,
+ "Placing stock on shelf": 2,
+ "Hold small product bag": 2,
+ "Carry container": 2,
+ "Pick up cleaning cloth": 2,
+ "Pick up product box": 2,
+ "Place box on shelf": 2,
+ "Place plush toy on shelf": 2,
+ "Adjust placement on shelf": 2,
+ "Move plush toy": 2,
+ "Arrange cardboard": 2,
+ "Walk with marker": 2,
+ "Pick up small object": 2,
+ "Walk across room": 2,
+ "Place cardboard square on stack": 2,
+ "Arrange cardboard squares": 2,
+ "Stacking cardboard squares": 2,
+ "Positioning cardboard on workspace": 2,
+ "Stacking cardboard square": 2,
+ "Stack cardboard square": 2,
+ "Stack cardboard squares": 2,
+ "Sorting paper stars": 2,
+ "Place star": 2,
+ "Place paper star": 2,
+ "Stop sorting stars": 2,
+ "Walk through doorway": 2,
+ "Pick up object": 2,
+ "Place item on table": 2,
+ "Sort and place paper star": 2,
+ "Place knife down": 2,
+ "Align cardboard strip": 2,
+ "Hold cardboard with ruler": 2,
+ "Move utility knife along ruler": 2,
+ "Slide utility knife along ruler": 2,
+ "Guide utility knife along ruler": 2,
+ "Place tool on table": 2,
+ "Move hand toward craft materials": 2,
+ "Manipulate paper strips": 2,
+ "Pick up blue paper strip": 2,
+ "Hold small object": 2,
+ "Place down strip": 2,
+ "Move hand away from workspace": 2,
+ "Lift pen and shift ruler": 2,
+ "Walk across the room": 2,
+ "Pack beads into box": 2,
+ "Pick up beads": 2,
+ "Pick up cardboard tray": 2,
+ "Move tray towards packing area": 2,
+ "Position cardboard tray": 2,
+ "Mark fabric with pen and ruler": 2,
+ "Gather cardboard pieces": 2,
+ "Examine canned goods": 2,
+ "Pick up Dior gift box": 2,
+ "Inspect Dior gift box": 2,
+ "Place back Dior gift box": 2,
+ "Move along the shelf": 2,
+ "Pick up another bottle": 2,
+ "Inspect bottle": 2,
+ "Inspect almond package": 2,
+ "Move along the supermarket aisle": 2,
+ "Pick up canned good": 2,
+ "Prepare to place cardboard": 2,
+ "Prepare to resume cutting": 2,
+ "Hold canned food": 2,
+ "Align canned food on shelf": 2,
+ "Place another canned food on shelf": 2,
+ "Adjust canned food on shelf": 2,
+ "Move hand away from shelf": 2,
+ "Move hand away": 2,
+ "Hold earbud case": 2,
+ "Open earbud case": 2,
+ "Hold electronic accessory": 2,
+ "Pick up accessory": 2,
+ "Pick up electronic accessory": 2,
+ "Move hand back to box": 2,
+ "Move towards box": 2,
+ "Hold items": 2,
+ "Place items on shelf": 2,
+ "Grasp snack package": 2,
+ "Hold snack package": 2,
+ "Hold snack packages": 2,
+ "Adjust snack package": 2,
+ "Align plastic containers": 2,
+ "Reach for items": 2,
+ "Adjust containers on shelf": 2,
+ "Adjust container position": 2,
+ "Grasp item": 2,
+ "Move item to bag": 2,
+ "Organize item on shelf": 2,
+ "Grasp shopping bag": 2,
+ "Organize bag contents": 2,
+ "Grasp and retrieve item": 2,
+ "Reposition sign and organize beads": 2,
+ "Draw lines with pen and ruler": 2,
+ "Place stars in container": 2,
+ "Adjust cardboard divider": 2,
+ "Put down phone": 2,
+ "Pick up water bottle": 2,
+ "Retrieve items from bag": 2,
+ "Remove item from bag": 2,
+ "Open paper lantern": 2,
+ "Fold paper lantern": 2,
+ "Grasp lantern": 2,
+ "Grasp lantern component": 2,
+ "Align paper lantern edges": 2,
+ "Adjust lantern string": 2,
+ "Handle paper lantern component": 2,
+ "Open folded paper lantern": 2,
+ "Adjust lantern shape": 2,
+ "Hold paper lantern": 2,
+ "Apply adhesive tape to lantern": 2,
+ "Open paper lantern component": 2,
+ "Expand paper lantern": 2,
+ "Align edges of paper lantern": 2,
+ "Carry cereal boxes": 2,
+ "Carry cereal towards aisle": 2,
+ "Carry pasta box towards aisle": 2,
+ "Hold container": 2,
+ "Carry item to shelf": 2,
+ "Inspect shelf": 2,
+ "Move to stock products": 2,
+ "Move to shelf base": 2,
+ "Pick up gift box": 2,
+ "Pick up next gift box": 2,
+ "Pick up snack pouch": 2,
+ "Move storage bin": 2,
+ "Hold bin and move through aisle": 2,
+ "Grasp plastic bag on shelf": 2,
+ "Arrange plastic containers": 2,
+ "Arrange container on shelf": 2,
+ "Sort Mahjong tiles": 2,
+ "Mark lines with pen along ruler": 2,
+ "Pick up charging case": 2,
+ "Inspect charging case": 2,
+ "Place charging case down": 2,
+ "Place ruler on cardboard": 2,
+ "Hold and align cardboard": 2,
+ "Reposition tools": 2,
+ "Position cardboard tube": 2,
+ "Position scissors for next cut": 2,
+ "Place canned good on shelf": 2,
+ "Move canned goods container": 2,
+ "Position container near shelf": 2,
+ "Place canned food in container": 2,
+ "Reach for next canned food item": 2,
+ "Move cardboard": 2,
+ "Labeling cardboard squares": 2,
+ "Labeling cardboard square": 2,
+ "Labeling cardboard piece": 2,
+ "Marking cardboard with pen": 2,
+ "Folding cardboard": 2,
+ "Manipulate cardboard sheet": 2,
+ "Record count on notepad": 2,
+ "Record star count on paper": 2,
+ "Pick up electronic device": 2,
+ "Place device on lap": 2,
+ "Move hand to paper stars": 2,
+ "Resume counting stars": 2,
+ "Reviewing count record": 2,
+ "Write on paper record": 2,
+ "Update paper record": 2,
+ "Reach for puzzle piece": 2,
+ "Sort puzzle pieces": 2,
+ "Approaching the table": 2,
+ "Preparing to craft": 2,
+ "Picking up crafting material": 2,
+ "Pick up small piece of material": 2,
+ "Manipulate material": 2,
+ "Place material": 2,
+ "Manipulate yellow strip": 2,
+ "Manipulating paper strips": 2,
+ "Manipulate bead": 2,
+ "Manipulate beads": 2,
+ "Hold and manipulate paper strip": 2,
+ "Sort canned goods in tray": 2,
+ "Move can towards shelf": 2,
+ "Wipe retail item": 2,
+ "Hold recording sheet and pen": 2,
+ "Record star count": 2,
+ "Hold pen and paper": 2,
+ "Observe surroundings": 2,
+ "Observe paper and count objects": 2,
+ "Write count on paper": 2,
+ "Place pen on table": 2,
+ "Place smartphone on table": 2,
+ "Resume writing on paper": 2,
+ "Place paper star in row": 2,
+ "Manipulate star": 2,
+ "Arrange paper stars": 2,
+ "Pick up power bank": 2,
+ "Pick up small item": 2,
+ "Walking to sink": 2,
+ "Washing hands": 2,
+ "Rub hands together": 2,
+ "Finish washing hands": 2,
+ "Pick up paper towel": 2,
+ "Dry hands": 2,
+ "Discard paper towel": 2,
+ "Release paper star": 2,
+ "Cut section from newspaper": 2,
+ "Tear newspaper": 2,
+ "Hold newspaper": 2,
+ "Hold and align newspaper": 2,
+ "Fold newspaper": 2,
+ "Reposition newspaper": 2,
+ "Sort blue star-shaped pieces": 2,
+ "Sort small plastic pieces": 2,
+ "Reach for more pieces": 2,
+ "Sort plastic pieces": 2,
+ "Typing on phone": 2,
+ "Repositioning ruler": 2,
+ "Place down ruler and pen": 2,
+ "Walk through hallway": 2,
+ "Fold cardboard edge": 2,
+ "Drop cardboard square into box": 2,
+ "Deposit cardboard squares": 2,
+ "Approaching work table": 2,
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+ "Cut cardboard sheet": 2,
+ "Wipe electronic item": 2,
+ "Place item in bag": 2,
+ "Select another item": 2,
+ "Pick up canned item": 2,
+ "Pick up another canned item": 2,
+ "Carry plastic container": 2,
+ "Pick up canned goods": 2,
+ "Move bin": 2,
+ "Walking along the aisle": 2,
+ "Observe stocking": 2,
+ "Place canned food in bin": 2,
+ "Pick up plastic container": 2,
+ "Forming quilled paper shape": 2,
+ "Manipulate quilled paper shape": 2,
+ "Place quilled paper shape": 2,
+ "Retrieve paper strip": 2,
+ "Select paper strip": 2,
+ "Manipulate quilled paper strips": 2,
+ "Transition to standing position": 2,
+ "Observe paper quilling station": 2,
+ "Sort quilled paper pieces": 2,
+ "Walk towards storage area": 2,
+ "Hold device and cable": 2,
+ "Move piece to pile": 2,
+ "Manipulate quilled paper": 2,
+ "Mark list with pen": 2,
+ "Mark paper list": 2,
+ "Adjust bead piles": 2,
+ "Sort blue beads": 2,
+ "Move blue beads": 2,
+ "Place down pen": 2,
+ "Walking through the office": 2,
+ "Place controller on table": 2,
+ "Resume sorting blue beads": 2,
+ "Finishing coil": 2,
+ "Folding paper strip": 2,
+ "Manipulate quilling paper": 2,
+ "Grasp electronic object": 2,
+ "Interaction with coworker": 2,
+ "Manipulate small object": 2,
+ "Manipulate paper quilling piece": 2,
+ "Hold quilled paper piece": 2,
+ "Hold and align paper strip": 2,
+ "Hold and rotate paper strip": 2,
+ "Move cardboard sheet": 2,
+ "Trim cardboard": 2,
+ "Return to sorting": 2,
+ "Record count": 2,
+ "Counting and organizing beads": 2,
+ "Pick up star bead": 2,
+ "Place and count bead": 2,
+ "Arrange star beads": 2,
+ "Counting star beads": 2,
+ "Retrieving more beads": 2,
+ "Adjust paper": 2,
+ "Gather star beads": 2,
+ "Arrange star beads for counting": 2,
+ "Sort and count beads": 2,
+ "Wipe food product": 1,
+ "Wipe jar": 1,
+ "Place pickle jar in box": 1,
+ "Release pickle jar": 1,
+ "Wipe the shelf": 1,
+ "Wipe the product jar": 1,
+ "Place jar into shelf box": 1,
+ "Wipe grocery shelf": 1,
+ "Align button in row": 1,
+ "Place button in row": 1,
+ "Pick up orange button": 1,
+ "Arrange small buttons": 1,
+ "Align button": 1,
+ "Align buttons": 1,
+ "Arrange button cluster": 1,
+ "Align button row": 1,
+ "Arrange buttons on table": 1,
+ "Look around the table": 1,
+ "Adjust red button in row": 1,
+ "Adjust red button": 1,
+ "Pull back hand": 1,
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+ "Reach for black button": 1,
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+ "Pick up black button": 1,
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+ "Place red button": 1,
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+ "Arrange red buttons": 1,
+ "Align red button in row": 1,
+ "Reach and sort buttons": 1,
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+ "Place and align button": 1,
+ "Move hand": 1,
+ "Move button to line": 1,
+ "Reach for utility knife": 1,
+ "Place smartphone on cardboard": 1,
+ "Walk towards room": 1,
+ "Retract camera/reposition view": 1,
+ "Switch to scissors": 1,
+ "Retract hand from bag": 1,
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+ "Select product from box": 1,
+ "Wipe ketchup bottle": 1,
+ "Prepare to place bottle on shelf": 1,
+ "Walk through office": 1,
+ "Transition to cutting": 1,
+ "Reposition hands and ruler": 1,
+ "Press fold": 1,
+ "Position utility knife on cardboard": 1,
+ "Place smartphone on stand": 1,
+ "Pick up dustpan": 1,
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+ "Move towards the stove": 1,
+ "Open stove pot lid": 1,
+ "Walking towards door": 1,
+ "Picking up bottle": 1,
+ "Wipe kitchen counter": 1,
+ "Rinse cloth in sink": 1,
+ "Move towards kitchen area": 1,
+ "Place cloth on floor": 1,
+ "Reach for cleaning supplies": 1,
+ "Remove cleaning bottle": 1,
+ "Washing hands in sink": 1,
+ "Wiping countertop": 1,
+ "Lift pot lid": 1,
+ "Stir contents": 1,
+ "Place lid back": 1,
+ "Move pot": 1,
+ "Place towel": 1,
+ "Use phone to check stock": 1,
+ "Place phone on shelf": 1,
+ "Remove item from shelf": 1,
+ "Sweep debris": 1,
+ "Sweep floor debris": 1,
+ "Place sauce in container": 1,
+ "Walk through store": 1,
+ "Reach for item on shelf": 1,
+ "Place oil in container": 1,
+ "Place supplement bottle in container": 1,
+ "Place spice jar in container": 1,
+ "Walking in the workspace": 1,
+ "Roll quilling paper": 1,
+ "Release paper coil": 1,
+ "Release and prepare new strip": 1,
+ "Reach for paper strips": 1,
+ "Place item into bag": 1,
+ "Position utility knife": 1,
+ "Lift utility knife": 1,
+ "Fold cut cardboard": 1,
+ "Look away": 1,
+ "Align scissors": 1,
+ "Position cardboard strip": 1,
+ "Inspect cardboard strip": 1,
+ "Pick up cut cardboard piece": 1,
+ "Move cardboard to pile": 1,
+ "Align cardboard piece": 1,
+ "Fold cardboard sheet": 1,
+ "Complete the cut": 1,
+ "Put down utility knife": 1,
+ "Hold utility knife": 1,
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+ "Place sauce bottle on shelf": 1,
+ "Align foam piece": 1,
+ "Pick up bottle": 1,
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+ "Assemble foam strips": 1,
+ "Adjust foam strip": 1,
+ "Align foam strip": 1,
+ "Attach foam strip": 1,
+ "Curve foam strip into loop": 1,
+ "Fold foam piece": 1,
+ "Pick up blue foam piece": 1,
+ "Hold foam pieces": 1,
+ "Peel foam strip": 1,
+ "Move small blue foam piece towards the strip": 1,
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+ "Lift blue strip": 1,
+ "Hold blue strip": 1,
+ "Peel blue strip": 1,
+ "Fold blue strip": 1,
+ "Align paper strip": 1,
+ "Interlock paper strips": 1,
+ "Pick up craft material": 1,
+ "Attach material to paper strip": 1,
+ "Pick up tool": 1,
+ "Enter workspace": 1,
+ "Grasp door handle": 1,
+ "Pick up supplies from box": 1,
+ "Enter the room": 1,
+ "Approach work table": 1,
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+ "Release hook": 1,
+ "Walk towards other aisles": 1,
+ "Reach for additional items": 1,
+ "Prepare to pick up item": 1,
+ "Reach for shelving divider": 1,
+ "Position shelving divider": 1,
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+ "Reach for product on shelf": 1,
+ "Release food item": 1,
+ "Reach for and examine canned goods": 1,
+ "Select and pick up a canned item": 1,
+ "Place item back on shelf": 1,
+ "Select a bottle": 1,
+ "Place bottle back on shelf": 1,
+ "Release bottle": 1,
+ "Scan supermarket shelves": 1,
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+ "Touch canned goods": 1,
+ "Reach for next can": 1,
+ "Retrieve next canned food item": 1,
+ "Reach for next canned food": 1,
+ "Retrieve canned food from box": 1,
+ "Pick up electronic accessory from box": 1,
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+ "Reach towards shelf": 1,
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+ "Place accessory box": 1,
+ "Pick up new electronic product": 1,
+ "Pick up electronic product": 1,
+ "Release product on shelf": 1,
+ "Pick up new product from box": 1,
+ "Pick up shopping bag": 1,
+ "Walk with shopping bag": 1,
+ "Pick up item from box": 1,
+ "Move box to next position": 1,
+ "Place snack package on shelf": 1,
+ "Place snack package in box": 1,
+ "Place snack in box": 1,
+ "Place snack packages on shelf": 1,
+ "Pick up snack packages": 1,
+ "Pick up snack package": 1,
+ "Organize snacks in box": 1,
+ "Reach for snack package": 1,
+ "Open cardboard box": 1,
+ "Remove cardboard flap": 1,
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+ "Place plush toy into bag": 1,
+ "Prepare to place item in bag": 1,
+ "Place item into shopping bag": 1,
+ "Realign Mahjong tiles": 1,
+ "Release lantern": 1,
+ "Pick up packaged paper lantern component": 1,
+ "Remove paper lantern part from packaging": 1,
+ "Remove plastic packaging": 1,
+ "Pick up food item": 1,
+ "Pick up cereal boxes": 1,
+ "Pick up pasta box": 1,
+ "Pick up container from box": 1,
+ "Reach for items in box": 1,
+ "Pick up grocery item": 1,
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+ "Retrieve snack from container": 1,
+ "Place gift box into bin": 1,
+ "Place gift box on shelf": 1,
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+ "Place snack pouch on shelf": 1,
+ "Remove storage bin from shelf": 1,
+ "Reach for empty shelf space": 1,
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+ "Remove plastic container from storage box": 1,
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+ "Stabilize cardboard": 1,
+ "Stabilize ruler": 1,
+ "Moving cardboard square": 1,
+ "Placing labeled square": 1,
+ "Starting to label next square": 1,
+ "Placing labeled cardboard square": 1,
+ "Switching marker": 1,
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+ "Placing pen on table": 1,
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+ "Pick up cardboard stack": 1,
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+ "Prepare to cut cardboard": 1,
+ "Score cardboard": 1,
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+ "Place storage container on floor": 1,
+ "Release container": 1,
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+ "Remove lid from container": 1,
+ "Place canned goods in container": 1,
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+ "Reorganize bin contents": 1,
+ "Rolling paper strip": 1,
+ "Selecting new paper strip": 1,
+ "Start folding paper strip": 1,
+ "Positioning paper strip": 1,
+ "Walk towards workspace": 1,
+ "Reach for paper strip": 1,
+ "Pull paper strip": 1,
+ "Sort beads and write count": 1
+ },
+ "action_options": [
+ "Adjust Mahjong tile",
+ "Adjust Mahjong tile alignment",
+ "Adjust Mahjong tile on the stack",
+ "Adjust Mahjong tiles",
+ "Adjust bead piles",
+ "Adjust canned food on shelf",
+ "Adjust cans in bin",
+ "Adjust cans in container",
+ "Adjust cans in tray",
+ "Adjust cardboard",
+ "Adjust cardboard divider",
+ "Adjust cardboard position",
+ "Adjust container on shelf",
+ "Adjust container position",
+ "Adjust containers on shelf",
+ "Adjust foam strip",
+ "Adjust grip",
+ "Adjust grip on container",
+ "Adjust hand position",
+ "Adjust item on shelf",
+ "Adjust lantern shape",
+ "Adjust lantern string",
+ "Adjust paper",
+ "Adjust paper strip",
+ "Adjust perspective",
+ "Adjust placement on shelf",
+ "Adjust position",
+ "Adjust pot position",
+ "Adjust puzzle piece",
+ "Adjust red button",
+ "Adjust red button in row",
+ "Adjust red button position",
+ "Adjust retail item position",
+ "Adjust retail items on shelf",
+ "Adjust ruler position",
+ "Adjust smartphone and sort pieces",
+ "Adjust snack package",
+ "Adjust tile row alignment",
+ "Adjust vacuum cleaner position",
+ "Adjusting a puzzle piece",
+ "Adjusting canned goods on shelf",
+ "Adjusting fabric for cutting",
+ "Adjusting fabric position",
+ "Adjusting puzzle piece",
+ "Align Mahjong tiles",
+ "Align and place retail item",
+ "Align blue strip",
+ "Align button",
+ "Align button in row",
+ "Align button row",
+ "Align buttons",
+ "Align canned food on shelf",
+ "Align canned goods on shelf",
+ "Align cardboard piece",
+ "Align cardboard strip",
+ "Align charging cable",
+ "Align edges of paper lantern",
+ "Align foam piece",
+ "Align foam strip",
+ "Align paper lantern edges",
+ "Align paper strip",
+ "Align plastic containers",
+ "Align red button in row",
+ "Align red buttons",
+ "Align ruler",
+ "Align ruler and mark cardboard",
+ "Align ruler on cardboard",
+ "Align ruler with crease",
+ "Align scissors",
+ "Apply adhesive tape to lantern",
+ "Approach boxes",
+ "Approach desk",
+ "Approach packing area",
+ "Approach restocking supplies",
+ "Approach table",
+ "Approach work table",
+ "Approach workstation",
+ "Approaching and pressing the door switch",
+ "Approaching the table",
+ "Approaching work table",
+ "Arrange Mahjong tiles",
+ "Arrange beads by color",
+ "Arrange black buttons",
+ "Arrange button cluster",
+ "Arrange buttons",
+ "Arrange buttons in a line",
+ "Arrange buttons in row",
+ "Arrange buttons on table",
+ "Arrange buttons on the table",
+ "Arrange canned products on shelf",
+ "Arrange cans in box",
+ "Arrange cans on shelf",
+ "Arrange cardboard",
+ "Arrange cardboard piece",
+ "Arrange cardboard pieces",
+ "Arrange cardboard squares",
+ "Arrange container on shelf",
+ "Arrange items on shelf",
+ "Arrange orange buttons",
+ "Arrange paper stars",
+ "Arrange paper strips",
+ "Arrange plastic containers",
+ "Arrange red buttons",
+ "Arrange small buttons",
+ "Arrange star beads",
+ "Arrange star beads for counting",
+ "Arrange star-shaped beads",
+ "Arrange tiles into row",
+ "Arrive at a different workstation",
+ "Assemble cardboard pieces",
+ "Assemble foam strips",
+ "Assess shelf arrangement",
+ "Attach foam strip",
+ "Attach material to paper strip",
+ "Attempt to fit puzzle piece",
+ "Begin folding paper strip",
+ "Begin rolling quilling strip",
+ "Bend and manipulate plastic strip",
+ "Browse and interact with phone interface",
+ "Browse mobile phone",
+ "Browse smartphone screen",
+ "Browsing mobile phone",
+ "Browsing smartphone content",
+ "Bundle display hooks",
+ "Cap marker",
+ "Carry cardboard piece",
+ "Carry cereal boxes",
+ "Carry cereal towards aisle",
+ "Carry container",
+ "Carry crate of cans",
+ "Carry item to shelf",
+ "Carry pasta box towards aisle",
+ "Carry plastic container",
+ "Carry stool to next shelf",
+ "Check phone",
+ "Check smart watch",
+ "Check watch",
+ "Clean shelf",
+ "Close cardboard box",
+ "Closing the door",
+ "Combine bead piles",
+ "Complete the cut",
+ "Connect cable to device",
+ "Continue cutting fabric",
+ "Continue cutting newspaper",
+ "Continue folding paper strip",
+ "Count and arrange paper stars",
+ "Count and record paper stars",
+ "Count paper stars",
+ "Counting and organizing beads",
+ "Counting star beads",
+ "Curve foam strip into loop",
+ "Cut along the edge of the newspaper",
+ "Cut along the line",
+ "Cut along the marked line",
+ "Cut along the newspaper edge",
+ "Cut cardboard",
+ "Cut cardboard along line",
+ "Cut cardboard grid",
+ "Cut cardboard into triangles",
+ "Cut cardboard pattern",
+ "Cut cardboard piece",
+ "Cut cardboard piece with scissors",
+ "Cut cardboard pieces with scissors",
+ "Cut cardboard shape",
+ "Cut cardboard sheet",
+ "Cut cardboard sheet with scissors",
+ "Cut cardboard square",
+ "Cut cardboard strip",
+ "Cut cardboard strip with scissors",
+ "Cut cardboard strip with utility knife",
+ "Cut cardboard triangle",
+ "Cut cardboard tube",
+ "Cut cardboard with scissors",
+ "Cut cardboard with utility knife",
+ "Cut fabric with scissors",
+ "Cut light green fabric",
+ "Cut newspaper",
+ "Cut newspaper with scissors",
+ "Cut out cardboard pattern",
+ "Cut section from newspaper",
+ "Cutting fabric",
+ "Deposit beads into box",
+ "Deposit cardboard squares",
+ "Discard item into bin",
+ "Discard paper towel",
+ "Draw grid line",
+ "Draw grid line with pen",
+ "Draw line",
+ "Draw line along ruler",
+ "Draw line on cardboard",
+ "Draw line with marker",
+ "Draw line with pen",
+ "Draw lines on cardboard",
+ "Draw lines with pen and ruler",
+ "Draw lines with ruler",
+ "Draw straight line",
+ "Draw straight lines on cardboard",
+ "Drawing grid line",
+ "Drawing grid line with pen and ruler",
+ "Drawing grid line with ruler",
+ "Drawing lines on cardboard",
+ "Drop cardboard square into box",
+ "Dry hands",
+ "Enter the room",
+ "Enter workspace",
+ "Entering the VR training room",
+ "Examine canned goods",
+ "Examine item",
+ "Examine labels",
+ "Examine product",
+ "Expand paper lantern",
+ "Extract wire hangers from box",
+ "Finish placing cardboard cutouts",
+ "Finish washing hands",
+ "Finish wiping and inspect jar",
+ "Finishing coil",
+ "Fold and manipulate ribbon",
+ "Fold and organize paper strips",
+ "Fold blue strip",
+ "Fold cardboard",
+ "Fold cardboard edge",
+ "Fold cardboard shape",
+ "Fold cardboard sheet",
+ "Fold cut cardboard",
+ "Fold foam piece",
+ "Fold lucky star",
+ "Fold newspaper",
+ "Fold paper lantern",
+ "Fold paper star",
+ "Fold paper strip",
+ "Fold paper strip into a star",
+ "Fold paper strip into knot",
+ "Fold paper strip into lucky star",
+ "Fold paper strip into star",
+ "Fold plastic strip",
+ "Fold purple paper",
+ "Fold purple paper strip",
+ "Fold ribbon",
+ "Folding cardboard",
+ "Folding paper strip",
+ "Forming quilled paper shape",
+ "Gather cardboard pieces",
+ "Gather pieces",
+ "Gather pieces into box",
+ "Gather star beads",
+ "Gathering colored beads",
+ "Gathering items",
+ "Gathering star beads",
+ "Gesturing",
+ "Grasp and retrieve item",
+ "Grasp cardboard sheet",
+ "Grasp cleaning bottle",
+ "Grasp door handle",
+ "Grasp electronic object",
+ "Grasp item",
+ "Grasp lantern",
+ "Grasp lantern component",
+ "Grasp next item",
+ "Grasp origami stars",
+ "Grasp package",
+ "Grasp paper strip",
+ "Grasp plastic bag on shelf",
+ "Grasp product from box",
+ "Grasp product from shelf",
+ "Grasp retail item",
+ "Grasp shopping bag",
+ "Grasp snack package",
+ "Grasping cleaning cloth",
+ "Greeting/acknowledging participants",
+ "Guide utility knife along ruler",
+ "Handle paper lantern component",
+ "Hold and align cardboard",
+ "Hold and align newspaper",
+ "Hold and align paper strip",
+ "Hold and bend paper strip",
+ "Hold and bend plastic strip",
+ "Hold and crease purple paper",
+ "Hold and examine item",
+ "Hold and inspect can",
+ "Hold and manipulate paper strip",
+ "Hold and mark cardboard piece",
+ "Hold and rotate paper strip",
+ "Hold and view phone",
+ "Hold and wipe product",
+ "Hold beads",
+ "Hold bin and move through aisle",
+ "Hold blue product box",
+ "Hold blue strip",
+ "Hold canned food",
+ "Hold cardboard",
+ "Hold cardboard piece",
+ "Hold cardboard pieces",
+ "Hold cardboard strip",
+ "Hold cardboard with ruler",
+ "Hold charger",
+ "Hold charger and cable",
+ "Hold charging cable",
+ "Hold cleaning cloth",
+ "Hold container",
+ "Hold container lid",
+ "Hold container of canned food",
+ "Hold craft tool",
+ "Hold device and cable",
+ "Hold earbud case",
+ "Hold electronic accessory",
+ "Hold electronic item",
+ "Hold empty container",
+ "Hold foam pieces",
+ "Hold instructional sign",
+ "Hold item",
+ "Hold item and adjust posture",
+ "Hold items",
+ "Hold items and inspect shelf",
+ "Hold items in hand",
+ "Hold newspaper",
+ "Hold paper lantern",
+ "Hold paper strip",
+ "Hold pen and paper",
+ "Hold phone",
+ "Hold pickle jar",
+ "Hold portable charger",
+ "Hold power adapter",
+ "Hold power bank and cable",
+ "Hold product",
+ "Hold product labels",
+ "Hold product package",
+ "Hold quilled paper coil",
+ "Hold quilled paper piece",
+ "Hold quilling paper",
+ "Hold recording sheet and pen",
+ "Hold ruler",
+ "Hold ruler and draw line",
+ "Hold ruler and mark cardboard",
+ "Hold ruler and marker",
+ "Hold ruler and pen steady",
+ "Hold ruler on cardboard",
+ "Hold ruler steady",
+ "Hold scissors",
+ "Hold small cardboard pieces",
+ "Hold small object",
+ "Hold small piece of ribbon",
+ "Hold small product bag",
+ "Hold small white box",
+ "Hold smartphone",
+ "Hold smartphone box",
+ "Hold snack package",
+ "Hold snack packages",
+ "Hold supplement bottle",
+ "Hold tray of canned goods",
+ "Hold utility knife",
+ "Hold water bottle",
+ "Holding marker",
+ "Identify next cardboard piece",
+ "Inflate paper star",
+ "Initiate star folding",
+ "Insert charging cable",
+ "Insert charging cable into power bank",
+ "Insert plug into power adapter",
+ "Inspect Dior gift box",
+ "Inspect almond package",
+ "Inspect and place item on shelf",
+ "Inspect bottle",
+ "Inspect cardboard piece",
+ "Inspect cardboard strip",
+ "Inspect charging case",
+ "Inspect electronic item",
+ "Inspect jar",
+ "Inspect product",
+ "Inspect product lid",
+ "Inspect shelf",
+ "Inspect shelf and organize stock",
+ "Inspect shelf condition",
+ "Inspect smartphone box",
+ "Inspect strip",
+ "Inspect supplement bottle",
+ "Interact with colleagues",
+ "Interact with phone",
+ "Interact with smartphone",
+ "Interact with smartphone screen",
+ "Interacting with phone screen",
+ "Interaction with coworker",
+ "Interlock paper strips",
+ "Labeling cardboard piece",
+ "Labeling cardboard square",
+ "Labeling cardboard squares",
+ "Lift blue strip",
+ "Lift pen and shift ruler",
+ "Lift pot lid",
+ "Lift utility knife",
+ "Lock phone",
+ "Look around the table",
+ "Look away",
+ "Manipulate adhesive strip",
+ "Manipulate and inspect colorful pieces",
+ "Manipulate bead",
+ "Manipulate beads",
+ "Manipulate cardboard piece",
+ "Manipulate cardboard shape",
+ "Manipulate cardboard sheet",
+ "Manipulate colorful pieces",
+ "Manipulate component",
+ "Manipulate component on strip",
+ "Manipulate craft paper strips",
+ "Manipulate craft piece",
+ "Manipulate folded paper star",
+ "Manipulate light blue strip",
+ "Manipulate material",
+ "Manipulate paper decoration",
+ "Manipulate paper edge",
+ "Manipulate paper piece",
+ "Manipulate paper quilling piece",
+ "Manipulate paper star",
+ "Manipulate paper stars",
+ "Manipulate paper strip",
+ "Manipulate paper strips",
+ "Manipulate plastic strip",
+ "Manipulate plastic strips",
+ "Manipulate power cable plug",
+ "Manipulate puzzle piece",
+ "Manipulate puzzle pieces",
+ "Manipulate quilled paper",
+ "Manipulate quilled paper shape",
+ "Manipulate quilled paper strip",
+ "Manipulate quilled paper strips",
+ "Manipulate quilling paper",
+ "Manipulate quilling strip",
+ "Manipulate ribbon knot",
+ "Manipulate ribbon piece",
+ "Manipulate small component",
+ "Manipulate small object",
+ "Manipulate small paper segment",
+ "Manipulate star",
+ "Manipulate yellow strip",
+ "Manipulating paper strips",
+ "Mark cardboard",
+ "Mark cardboard piece",
+ "Mark cardboard strip with pen",
+ "Mark cardboard with marker",
+ "Mark cardboard with pen",
+ "Mark cardboard with pen and ruler",
+ "Mark cardboard with ruler",
+ "Mark cardboard with ruler and pen",
+ "Mark fabric",
+ "Mark fabric with pen",
+ "Mark fabric with pen and ruler",
+ "Mark line on cardboard",
+ "Mark lines on cardboard",
+ "Mark lines with marker",
+ "Mark lines with pen along ruler",
+ "Mark list with pen",
+ "Mark paper list",
+ "Mark straight line",
+ "Marking cardboard piece",
+ "Marking cardboard with pen",
+ "Marking lines on cardboard",
+ "Measure and mark cardboard",
+ "Measure cardboard with ruler",
+ "Move Mahjong tile",
+ "Move along shelf",
+ "Move along the shelf",
+ "Move along the shelves",
+ "Move along the supermarket aisle",
+ "Move and place black buttons",
+ "Move away from collection box",
+ "Move away from desk",
+ "Move away from shelf",
+ "Move away from table",
+ "Move away from workstation",
+ "Move bin",
+ "Move bin to shelf area",
+ "Move black button",
+ "Move blue beads",
+ "Move box to next position",
+ "Move button to line",
+ "Move camera over surface",
+ "Move can towards shelf",
+ "Move canned goods container",
+ "Move cardboard",
+ "Move cardboard box",
+ "Move cardboard piece",
+ "Move cardboard sheet",
+ "Move cardboard to pile",
+ "Move container toward shelf",
+ "Move dustpan to side",
+ "Move hand",
+ "Move hand away",
+ "Move hand away from shelf",
+ "Move hand away from workspace",
+ "Move hand back to box",
+ "Move hand over button pile",
+ "Move hand to paper stars",
+ "Move hand toward craft materials",
+ "Move item to bag",
+ "Move marker and adjust hand",
+ "Move marker and ruler",
+ "Move marker away",
+ "Move orange buttons",
+ "Move origami stars",
+ "Move pen",
+ "Move pen aside",
+ "Move pen away",
+ "Move phone",
+ "Move piece to pile",
+ "Move pieces into box",
+ "Move pineapple chips",
+ "Move plastic storage bin",
+ "Move plush toy",
+ "Move pot",
+ "Move product to box",
+ "Move product to shelf",
+ "Move product towards shelf",
+ "Move puzzle piece",
+ "Move ruler",
+ "Move ruler and tools",
+ "Move scissors away",
+ "Move small blue foam piece towards the strip",
+ "Move smartphone",
+ "Move storage bin",
+ "Move through aisle",
+ "Move through the training room",
+ "Move to box",
+ "Move to desk",
+ "Move to next section",
+ "Move to shelf",
+ "Move to shelf base",
+ "Move to stock products",
+ "Move towards aisle",
+ "Move towards box",
+ "Move towards kitchen area",
+ "Move towards shelf",
+ "Move towards table",
+ "Move towards the stove",
+ "Move tray towards packing area",
+ "Move utility knife along ruler",
+ "Move vacuum cleaner",
+ "Move vacuum cleaner hose",
+ "Moving cardboard square",
+ "Moving hand",
+ "Moving hand towards cardboard stack",
+ "Moving ruler",
+ "Observe and pause",
+ "Observe and walk through store",
+ "Observe colleague and workspace",
+ "Observe craft layout",
+ "Observe desktop layout",
+ "Observe paper and count objects",
+ "Observe paper quilling station",
+ "Observe puzzle progress",
+ "Observe room",
+ "Observe shelf",
+ "Observe shelf status",
+ "Observe sorting progress",
+ "Observe stocking",
+ "Observe surroundings",
+ "Observe workspace",
+ "Open cardboard box",
+ "Open door",
+ "Open earbud case",
+ "Open folded paper lantern",
+ "Open paper lantern",
+ "Open paper lantern component",
+ "Open small case",
+ "Open stove pot lid",
+ "Open supplement bottle",
+ "Operate smartphone",
+ "Organize bag contents",
+ "Organize cardboard pieces",
+ "Organize item on shelf",
+ "Organize products",
+ "Organize snacks in box",
+ "Organize tools and materials",
+ "Pack beads into box",
+ "Peel blue strip",
+ "Peel foam strip",
+ "Pick up Dior gift box",
+ "Pick up Mahjong tile",
+ "Pick up accessory",
+ "Pick up and sort cardboard",
+ "Pick up another bottle",
+ "Pick up another canned item",
+ "Pick up another item",
+ "Pick up beads",
+ "Pick up black button",
+ "Pick up blue foam piece",
+ "Pick up blue paper strip",
+ "Pick up bottle",
+ "Pick up bottled sauce",
+ "Pick up button",
+ "Pick up can",
+ "Pick up canned food",
+ "Pick up canned good",
+ "Pick up canned goods",
+ "Pick up canned item",
+ "Pick up canned product",
+ "Pick up cardboard",
+ "Pick up cardboard cutout",
+ "Pick up cardboard piece",
+ "Pick up cardboard square",
+ "Pick up cardboard stack",
+ "Pick up cardboard strip",
+ "Pick up cardboard tray",
+ "Pick up cereal boxes",
+ "Pick up charging cable",
+ "Pick up charging case",
+ "Pick up cleaning cloth",
+ "Pick up colored tile",
+ "Pick up container",
+ "Pick up container from box",
+ "Pick up craft material",
+ "Pick up cut cardboard piece",
+ "Pick up dustpan",
+ "Pick up electronic accessory",
+ "Pick up electronic accessory from box",
+ "Pick up electronic device",
+ "Pick up electronic item",
+ "Pick up electronic product",
+ "Pick up food item",
+ "Pick up gift box",
+ "Pick up grocery item",
+ "Pick up item",
+ "Pick up item from bin",
+ "Pick up item from box",
+ "Pick up item from shelf",
+ "Pick up items from the shopping bag",
+ "Pick up jar",
+ "Pick up light blue strip",
+ "Pick up marker",
+ "Pick up metal ruler",
+ "Pick up new cardboard piece",
+ "Pick up new electronic product",
+ "Pick up new product from box",
+ "Pick up next gift box",
+ "Pick up next item from bin",
+ "Pick up next product from bin",
+ "Pick up nut bar box",
+ "Pick up object",
+ "Pick up oil bottle",
+ "Pick up orange button",
+ "Pick up pack from shelf",
+ "Pick up packaged paper lantern component",
+ "Pick up paper star",
+ "Pick up paper strip",
+ "Pick up paper towel",
+ "Pick up pasta box",
+ "Pick up pen",
+ "Pick up phone",
+ "Pick up pickle jar",
+ "Pick up pink water bottle",
+ "Pick up plastic bin",
+ "Pick up plastic container",
+ "Pick up plush toy",
+ "Pick up portable charger",
+ "Pick up power bank",
+ "Pick up product",
+ "Pick up product box",
+ "Pick up product from bin",
+ "Pick up product from box",
+ "Pick up product from shelf",
+ "Pick up puzzle piece",
+ "Pick up red button",
+ "Pick up retail item",
+ "Pick up sauce bottle",
+ "Pick up scissors",
+ "Pick up shopping bag",
+ "Pick up small cardboard piece",
+ "Pick up small item",
+ "Pick up small object",
+ "Pick up small piece of material",
+ "Pick up smartphone",
+ "Pick up snack package",
+ "Pick up snack packages",
+ "Pick up snack packs",
+ "Pick up snack pouch",
+ "Pick up spice jar",
+ "Pick up stapler",
+ "Pick up star",
+ "Pick up star bead",
+ "Pick up star-shaped bead",
+ "Pick up storage container",
+ "Pick up supplement bottle",
+ "Pick up supplies from box",
+ "Pick up tin can",
+ "Pick up tool",
+ "Pick up utility knife",
+ "Pick up water bottle",
+ "Pick up yellow item",
+ "Pick up yellow paper strip",
+ "Picking up bottle",
+ "Picking up crafting material",
+ "Picking up stock",
+ "Place Mahjong tile on stack",
+ "Place Mahjong tile on the stack",
+ "Place accessory box",
+ "Place accessory into box",
+ "Place accessory on shelf",
+ "Place and align button",
+ "Place and count bead",
+ "Place another canned food on shelf",
+ "Place back Dior gift box",
+ "Place bead on table",
+ "Place bottle back on shelf",
+ "Place box on shelf",
+ "Place button",
+ "Place button in group",
+ "Place button in row",
+ "Place can on shelf",
+ "Place canned food in bin",
+ "Place canned food in container",
+ "Place canned food on shelf",
+ "Place canned good on shelf",
+ "Place canned goods in container",
+ "Place canned product on shelf",
+ "Place cans into box",
+ "Place cardboard",
+ "Place cardboard piece",
+ "Place cardboard piece on stack",
+ "Place cardboard square",
+ "Place cardboard square on stack",
+ "Place cardboard strip",
+ "Place charger on table",
+ "Place charging case down",
+ "Place cloth on floor",
+ "Place colored tile",
+ "Place container in bin",
+ "Place container on floor",
+ "Place container on shelf",
+ "Place controller on table",
+ "Place crate on floor",
+ "Place device on lap",
+ "Place down paper pieces",
+ "Place down paper segment",
+ "Place down pen",
+ "Place down pink water bottle",
+ "Place down ruler and pen",
+ "Place down scissors",
+ "Place down strip",
+ "Place finished star on table",
+ "Place gift box into bin",
+ "Place gift box on shelf",
+ "Place hand on table",
+ "Place item back",
+ "Place item back on shelf",
+ "Place item in bag",
+ "Place item in container",
+ "Place item in shopping bag",
+ "Place item into bag",
+ "Place item into shopping bag",
+ "Place item on shelf",
+ "Place item on table",
+ "Place items on shelf",
+ "Place items on table",
+ "Place items on the shelf",
+ "Place jar in box",
+ "Place jar into shelf box",
+ "Place jar on shelf",
+ "Place ketchup bottle on shelf",
+ "Place knife down",
+ "Place lid back",
+ "Place marked piece down",
+ "Place marker down",
+ "Place material",
+ "Place oil in container",
+ "Place paper star",
+ "Place paper star in row",
+ "Place pen on cardboard",
+ "Place pen on table",
+ "Place phone down",
+ "Place phone on desk",
+ "Place phone on shelf",
+ "Place phone on table",
+ "Place pickle jar in box",
+ "Place piece into puzzle",
+ "Place plush toy into bag",
+ "Place plush toy on shelf",
+ "Place product in box",
+ "Place product on shelf",
+ "Place puzzle piece",
+ "Place quilled paper shape",
+ "Place red button",
+ "Place ribbon onto project",
+ "Place ruler on cardboard",
+ "Place sauce bottle on shelf",
+ "Place sauce in container",
+ "Place scissors aside",
+ "Place scissors down",
+ "Place scissors on table",
+ "Place smartphone down",
+ "Place smartphone on cardboard",
+ "Place smartphone on desk",
+ "Place smartphone on stand",
+ "Place smartphone on table",
+ "Place snack in box",
+ "Place snack on shelf",
+ "Place snack package in box",
+ "Place snack package on shelf",
+ "Place snack packages on shelf",
+ "Place snack pouch in container",
+ "Place snack pouch on shelf",
+ "Place spice jar in container",
+ "Place star",
+ "Place star in row",
+ "Place star on table",
+ "Place stars in container",
+ "Place stool on floor",
+ "Place storage container on floor",
+ "Place strip on table",
+ "Place supplement bottle in container",
+ "Place tool on table",
+ "Place towel",
+ "Place water bottle on table",
+ "Place white box on table",
+ "Placing labeled cardboard square",
+ "Placing labeled square",
+ "Placing paper strip",
+ "Placing pen on table",
+ "Placing phone down",
+ "Placing piece on stack",
+ "Placing stock on shelf",
+ "Plug cable into portable charger",
+ "Position cardboard for cutting",
+ "Position cardboard piece",
+ "Position cardboard strip",
+ "Position cardboard tray",
+ "Position cardboard tube",
+ "Position container near shelf",
+ "Position container on shelf",
+ "Position hands for work",
+ "Position ribbon piece",
+ "Position ruler and mark cardboard",
+ "Position ruler on cardboard",
+ "Position scissors",
+ "Position scissors for next cut",
+ "Position scissors to cut cardboard",
+ "Position shelving divider",
+ "Position the ruler",
+ "Position tray",
+ "Position utility knife",
+ "Position utility knife on cardboard",
+ "Positioning cardboard on workspace",
+ "Positioning paper strip",
+ "Positioning puzzle piece",
+ "Positioning ruler on cardboard",
+ "Prepare paper strip",
+ "Prepare to cut cardboard",
+ "Prepare to draw lines",
+ "Prepare to pick up item",
+ "Prepare to place bottle on shelf",
+ "Prepare to place cardboard",
+ "Prepare to place item in bag",
+ "Prepare to place product",
+ "Prepare to resume cutting",
+ "Prepare to sort beads",
+ "Preparing to craft",
+ "Press fold",
+ "Pull back hand",
+ "Pull paper strip",
+ "Push vacuum cleaner",
+ "Put down phone",
+ "Put down scissors",
+ "Put down smartphone",
+ "Put down utility knife",
+ "Put down water bottle",
+ "Putting away smartphone",
+ "Reach and sort buttons",
+ "Reach for Mahjong tiles",
+ "Reach for additional items",
+ "Reach for and examine canned goods",
+ "Reach for and pick up smartphone",
+ "Reach for another container",
+ "Reach for another item",
+ "Reach for beads",
+ "Reach for black button",
+ "Reach for button",
+ "Reach for can",
+ "Reach for canned food",
+ "Reach for canned goods",
+ "Reach for cardboard box",
+ "Reach for cardboard piece",
+ "Reach for cleaning supplies",
+ "Reach for container",
+ "Reach for craft items",
+ "Reach for empty shelf space",
+ "Reach for item",
+ "Reach for item in box",
+ "Reach for item on shelf",
+ "Reach for items",
+ "Reach for items in box",
+ "Reach for more pieces",
+ "Reach for next can",
+ "Reach for next canned food",
+ "Reach for next canned food item",
+ "Reach for next canned product",
+ "Reach for next item",
+ "Reach for next piece",
+ "Reach for next product",
+ "Reach for object",
+ "Reach for paper strip",
+ "Reach for paper strips",
+ "Reach for phone",
+ "Reach for product",
+ "Reach for product labels",
+ "Reach for product on shelf",
+ "Reach for puzzle piece",
+ "Reach for retail item",
+ "Reach for shelf",
+ "Reach for shelving divider",
+ "Reach for snack package",
+ "Reach for snack pouch",
+ "Reach for star",
+ "Reach for stars",
+ "Reach for utility knife",
+ "Reach for water bottle",
+ "Reach for wire hangers",
+ "Reach into bag",
+ "Reach into box",
+ "Reach towards shelf",
+ "Reaching for beads",
+ "Realign Mahjong tiles",
+ "Rearrange Mahjong tile",
+ "Rearrange Mahjong tiles",
+ "Rearrange shelf item",
+ "Record count",
+ "Record count on notepad",
+ "Record star count",
+ "Record star count on paper",
+ "Release and prepare new strip",
+ "Release bottle",
+ "Release cardboard",
+ "Release cardboard piece",
+ "Release cardboard piece and gesture",
+ "Release cardboard shape",
+ "Release container",
+ "Release folded paper",
+ "Release food item",
+ "Release hook",
+ "Release label",
+ "Release lantern",
+ "Release paper",
+ "Release paper coil",
+ "Release paper star",
+ "Release paper strip",
+ "Release pickle jar",
+ "Release product on shelf",
+ "Release puzzle piece",
+ "Release quilling strip",
+ "Release scissors",
+ "Release smartphone",
+ "Remove cardboard flap",
+ "Remove cardboard pattern",
+ "Remove cardboard pattern piece",
+ "Remove cleaning bottle",
+ "Remove item from bag",
+ "Remove item from shelf",
+ "Remove lid from container",
+ "Remove paper lantern part from packaging",
+ "Remove plastic container from shelf",
+ "Remove plastic container from storage box",
+ "Remove plastic packaging",
+ "Remove ruler",
+ "Remove ruler and marker",
+ "Remove shelf label",
+ "Remove storage bin from shelf",
+ "Reorganize bin contents",
+ "Reposition and cut",
+ "Reposition cardboard for cutting",
+ "Reposition hand",
+ "Reposition hands",
+ "Reposition hands and ruler",
+ "Reposition marker",
+ "Reposition newspaper",
+ "Reposition pen and prepare for next line",
+ "Reposition ruler",
+ "Reposition ruler and pen",
+ "Reposition scissors",
+ "Reposition sign and organize beads",
+ "Reposition tools",
+ "Reposition utility knife",
+ "Repositioning ruler",
+ "Repositioning ruler and cardboard",
+ "Resume counting stars",
+ "Resume observation",
+ "Resume sorting blue beads",
+ "Resume writing on paper",
+ "Retract camera/reposition view",
+ "Retract hand",
+ "Retract hand from bag",
+ "Retrieve another container",
+ "Retrieve canned food from box",
+ "Retrieve hand to table",
+ "Retrieve items from bag",
+ "Retrieve next canned food item",
+ "Retrieve paper strip",
+ "Retrieve paper strips",
+ "Retrieve snack from container",
+ "Retrieve star",
+ "Retrieving more beads",
+ "Return to sorting",
+ "Reviewing count record",
+ "Rinse cloth in sink",
+ "Roll quilling paper",
+ "Rolling paper strip",
+ "Rub hands together",
+ "Scan for next piece",
+ "Scan supermarket shelves",
+ "Score cardboard",
+ "Scroll on smartphone",
+ "Scroll smartphone screen",
+ "Scroll through photo gallery",
+ "Scrolling and viewing content on phone",
+ "Scrolling or navigating on phone",
+ "Search for puzzle piece",
+ "Secure paper edges with adhesive",
+ "Secure ribbon with needle",
+ "Securing paper structure",
+ "Select a bottle",
+ "Select and pick up a canned item",
+ "Select another item",
+ "Select paper strip",
+ "Select product from box",
+ "Selecting new paper strip",
+ "Separate cardboard piece",
+ "Set down scissors and pick up power bank",
+ "Set down utility knife",
+ "Slide utility knife along ruler",
+ "Sort Mahjong tiles",
+ "Sort and adjust button line",
+ "Sort and arrange buttons",
+ "Sort and arrange cardboard pieces",
+ "Sort and count beads",
+ "Sort and place buttons",
+ "Sort and place paper star",
+ "Sort and stack cardboard pieces",
+ "Sort beads",
+ "Sort beads and write count",
+ "Sort beads by color",
+ "Sort beads by hand",
+ "Sort beads on table",
+ "Sort beads on the table",
+ "Sort blue beads",
+ "Sort blue star-shaped pieces",
+ "Sort button",
+ "Sort button by color",
+ "Sort buttons",
+ "Sort buttons by color",
+ "Sort canned goods in tray",
+ "Sort colored tiles",
+ "Sort colorful pieces",
+ "Sort craft items",
+ "Sort cut cardboard",
+ "Sort light blue origami stars",
+ "Sort orange button",
+ "Sort orange buttons",
+ "Sort origami stars",
+ "Sort origami stars by color",
+ "Sort paper star",
+ "Sort paper stars",
+ "Sort plastic pieces",
+ "Sort purple beads",
+ "Sort purple star-shaped objects",
+ "Sort puzzle pieces",
+ "Sort quilled paper pieces",
+ "Sort small colorful pieces",
+ "Sort small craft pieces",
+ "Sort small objects",
+ "Sort small plastic pieces",
+ "Sort star-shaped beads",
+ "Sort star-shaped objects",
+ "Sort star-shaped objects by color",
+ "Sort tiles",
+ "Sort tiles by color",
+ "Sort yellow star-shaped objects",
+ "Sorting buttons",
+ "Sorting colorful paper pieces",
+ "Sorting paper stars",
+ "Stabilize cardboard",
+ "Stabilize ruler",
+ "Stack cardboard pieces",
+ "Stack cardboard square",
+ "Stack cardboard squares",
+ "Stacking cardboard pieces",
+ "Stacking cardboard square",
+ "Stacking cardboard squares",
+ "Stand up and walk away",
+ "Start cutting",
+ "Start folding paper strip",
+ "Starting to label next square",
+ "Stir contents",
+ "Stop measuring and put down tools",
+ "Stop sorting stars",
+ "Sweep debris",
+ "Sweep floor debris",
+ "Switch to scissors",
+ "Switching marker",
+ "Tap smartphone screen",
+ "Tapping on smartphone screen",
+ "Tapping smartphone screen",
+ "Tear newspaper",
+ "Tear off cardboard segment",
+ "Touch canned goods",
+ "Touch pieces in box",
+ "Touch shelf edge",
+ "Trace pattern on cardboard",
+ "Transition to cutting",
+ "Transition to standing position",
+ "Trim cardboard",
+ "Trim cardboard piece",
+ "Type on smartphone",
+ "Typing message on smartphone",
+ "Typing on phone",
+ "Typing on smartphone",
+ "Update paper record",
+ "Use phone",
+ "Use phone to check instructions",
+ "Use phone to check stock",
+ "Use phone while crafting",
+ "Use smartphone",
+ "Vacuum edge of carpet",
+ "Vacuum the carpet",
+ "Vacuuming along the wall edge",
+ "Vacuuming carpet corner",
+ "Vacuuming carpet edge",
+ "Vacuuming the carpet edge",
+ "View content on smartphone",
+ "View phone screen",
+ "Viewing phone screen",
+ "Walk across office",
+ "Walk across room",
+ "Walk across the room",
+ "Walk away",
+ "Walk in hallway",
+ "Walk through corridor",
+ "Walk through doorway",
+ "Walk through hallway",
+ "Walk through office",
+ "Walk through store",
+ "Walk through workspace",
+ "Walk towards aisle",
+ "Walk towards desk",
+ "Walk towards next aisle",
+ "Walk towards other aisles",
+ "Walk towards room",
+ "Walk towards shelf",
+ "Walk towards shelves",
+ "Walk towards storage area",
+ "Walk towards table",
+ "Walk towards workspace",
+ "Walk with cardboard",
+ "Walk with cardboard cutout",
+ "Walk with marker",
+ "Walk with shopping bag",
+ "Walking across the room",
+ "Walking along the aisle",
+ "Walking in the hallway",
+ "Walking in the workspace",
+ "Walking through classroom",
+ "Walking through office hallway",
+ "Walking through the office",
+ "Walking to sink",
+ "Walking towards door",
+ "Walking towards workstation",
+ "Washing hands",
+ "Washing hands in sink",
+ "Wipe down shelf",
+ "Wipe electronic item",
+ "Wipe food product",
+ "Wipe grocery shelf",
+ "Wipe item",
+ "Wipe jar",
+ "Wipe ketchup bottle",
+ "Wipe kitchen counter",
+ "Wipe product",
+ "Wipe retail item",
+ "Wipe shelf",
+ "Wipe shelf surface",
+ "Wipe the plastic jar",
+ "Wipe the product jar",
+ "Wipe the shelf",
+ "Wiping countertop",
+ "Withdraw hand",
+ "Write count on paper",
+ "Write on notepad",
+ "Write on paper",
+ "Write on paper record",
+ "Writing on notepad",
+ "fold purple ribbon",
+ "sort craft materials"
+ ],
+ "subtask_options": [
+ "Adding items to shopping container",
+ "Adjust Mahjong tiles",
+ "Adjust and align Mahjong tiles",
+ "Adjust and cut fabric",
+ "Adjust fabric for cutting",
+ "Adjust lantern string and handle components",
+ "Adjust position and check phone",
+ "Adjust tile alignment",
+ "Adjust tiles on stack",
+ "Adjust, move, and realign Mahjong tiles",
+ "Adjusting a puzzle piece",
+ "Adjusting and folding cardboard",
+ "Adjusting and placing down paper pieces",
+ "Adjusting and securing paper structure",
+ "Adjusting canned goods on the shelf",
+ "Adjusting cardboard divider",
+ "Adjusting cardboard layout",
+ "Adjusting container positions",
+ "Adjusting cookware",
+ "Adjusting edge and marking cardboard",
+ "Adjusting items and reaching for stock",
+ "Adjusting items on shelf",
+ "Adjusting items on the shelf",
+ "Adjusting marker and ruler",
+ "Adjusting paper edge and placing strip",
+ "Adjusting posture while holding item",
+ "Adjusting puzzle piece",
+ "Adjusting retail items on shelf",
+ "Adjusting ruler position",
+ "Adjusting snack package",
+ "Adjusting stock and finishing placement",
+ "Align and fold newspaper",
+ "Align canned goods on shelf",
+ "Align paper lantern edges",
+ "Align ruler and draw line",
+ "Aligning button rows",
+ "Aligning canned goods on the shelf",
+ "Aligning cardboard for cutting",
+ "Aligning cardboard strip",
+ "Aligning plastic containers on the shelf",
+ "Aligning ruler for final measurements",
+ "Approach inventory boxes",
+ "Approaching restocking supplies",
+ "Approaching the stove",
+ "Approaching workstation",
+ "Arrange Mahjong tiles",
+ "Arrange buttons",
+ "Arrange buttons in a line",
+ "Arrange paper strips",
+ "Arrange tiles into row",
+ "Arranging and marking cardboard strips",
+ "Arranging buttons",
+ "Arranging buttons on the table",
+ "Arranging cardboard squares",
+ "Arranging items on shelf",
+ "Arranging orange buttons",
+ "Arranging paper stars",
+ "Arranging products on shelf",
+ "Arranging shelf display",
+ "Arranging star-shaped beads",
+ "Assembling cardboard base",
+ "Assembling cardboard boxes",
+ "Assembling material pieces",
+ "Assembling small decorative components",
+ "Assembling the foam base loop",
+ "Assessing shelf arrangement",
+ "Assessing shelf status and relocating",
+ "Attaching and folding blue foam strips",
+ "Bagging a held electronic item",
+ "Beginning to roll the quilling strip",
+ "Bend and shape paper strips",
+ "Bending plastic strip",
+ "Boxing pieces and picking up phone",
+ "Browse and interact with phone interface",
+ "Browse mobile phone",
+ "Browse mobile phone and cut newspaper",
+ "Browsing and selecting canned goods",
+ "Browsing phone interface",
+ "Browsing photo gallery",
+ "Browsing smartphone",
+ "Browsing smartphone content",
+ "Bundle display hooks",
+ "Cap marker and place down",
+ "Capping marker and positioning ruler",
+ "Carry cereal boxes to aisle",
+ "Carry shopping bag",
+ "Charging power bank",
+ "Check instructions on phone",
+ "Checking cooking pot",
+ "Checking smartphone",
+ "Checking smartwatch while reaching for product",
+ "Checking stock information",
+ "Clean and inspect shelf",
+ "Clean shelf and stock product",
+ "Clean shelf surface",
+ "Cleaning and boxing jars",
+ "Cleaning and organizing products in boxes",
+ "Cleaning cloth maintenance",
+ "Cleaning kitchen surfaces",
+ "Cleaning products and retrieving items from boxes",
+ "Cleaning shelves and handling pickle jars",
+ "Cleaning shelves and rearranging products",
+ "Cleaning shelves and relocating",
+ "Cleaning up workspace and moving items",
+ "Cleaning up workstation",
+ "Cleaning workspace",
+ "Cleaning, bagging, and selecting another item",
+ "Cleaning, inspecting, and bagging an electronic item",
+ "Clear workspace and pick up phone",
+ "Clearing space on the shelf",
+ "Collecting canned food into bin",
+ "Collecting origami stars",
+ "Comparing and replacing bottles",
+ "Complete folding and place star on table",
+ "Completing list and moving away from desk",
+ "Completing marking and stepping away",
+ "Connecting power cables to a portable charger",
+ "Cooking at the stove",
+ "Count beads and retrieve more",
+ "Counting and recording paper stars",
+ "Counting and recording stars",
+ "Crafting with paper strips",
+ "Cut along the marked line",
+ "Cut along the newspaper edge",
+ "Cut and fold cardboard",
+ "Cut and release cardboard",
+ "Cut and reposition utility knife",
+ "Cut and tear off cardboard segment",
+ "Cut cardboard",
+ "Cut cardboard pattern",
+ "Cut cardboard with utility knife",
+ "Cut fabric with scissors",
+ "Cut light green fabric",
+ "Cut light green fabric and reposition scissors",
+ "Cut newspaper and place scissors on table",
+ "Cut out cardboard pattern",
+ "Cut section from newspaper",
+ "Cutting and adjusting cardboard",
+ "Cutting and adjusting cardboard pieces",
+ "Cutting and adjusting cardboard sheet",
+ "Cutting and adjusting scissors",
+ "Cutting and folding cardboard",
+ "Cutting and folding cardboard shapes",
+ "Cutting and gathering cardboard",
+ "Cutting and measuring cardboard",
+ "Cutting and organizing cardboard pieces",
+ "Cutting and pausing",
+ "Cutting and picking up new cardboard",
+ "Cutting and placing cardboard",
+ "Cutting and placing cardboard piece",
+ "Cutting and placing cardboard squares",
+ "Cutting and preparing cardboard pieces",
+ "Cutting and releasing cardboard piece",
+ "Cutting and releasing cardboard shapes",
+ "Cutting and repositioning cardboard",
+ "Cutting and separating cardboard pieces",
+ "Cutting and sorting cardboard squares",
+ "Cutting and stacking cardboard pieces",
+ "Cutting cardboard",
+ "Cutting cardboard and picking up smartphone",
+ "Cutting cardboard and placing down scissors",
+ "Cutting cardboard and placing knife down",
+ "Cutting cardboard and putting down scissors",
+ "Cutting cardboard and retrieving power bank",
+ "Cutting cardboard into strips",
+ "Cutting cardboard into triangles",
+ "Cutting cardboard piece",
+ "Cutting cardboard pieces",
+ "Cutting cardboard pieces with scissors",
+ "Cutting cardboard shapes",
+ "Cutting cardboard sheet",
+ "Cutting cardboard square",
+ "Cutting cardboard strip",
+ "Cutting cardboard strips",
+ "Cutting cardboard strips with scissors",
+ "Cutting cardboard triangles",
+ "Cutting cardboard tube",
+ "Cutting cardboard tube and setting aside scissors",
+ "Cutting cardboard with a utility knife",
+ "Cutting cardboard with scissors",
+ "Cutting cardboard with scissors and checking phone",
+ "Cutting cardboard with utility knife",
+ "Cutting initial cardboard pieces",
+ "Cutting newspaper",
+ "Cutting triangular cardboard pieces",
+ "Depositing cardboard squares",
+ "Document bead counts",
+ "Draw and reposition ruler",
+ "Draw lines and patterns on cardboard",
+ "Draw lines using a ruler",
+ "Drawing grid lines",
+ "Drawing grid lines and repositioning cardboard",
+ "Drawing grid lines with a pen",
+ "Drawing grid lines with a ruler",
+ "Drawing grid lines with a ruler and pen",
+ "Drawing guide lines on cardboard",
+ "Drawing lines along ruler",
+ "Drawing lines and checking smartphone",
+ "Drawing lines on cardboard",
+ "Entering the training area",
+ "Examine yellow item",
+ "Examining a product",
+ "Expand and adjust lantern shape",
+ "Expand paper lantern",
+ "Extract wire hangers from inventory",
+ "Fetching materials",
+ "Final alignment and marking",
+ "Final alignment of lantern edges",
+ "Final button arrangement",
+ "Final cardboard cutting",
+ "Final component assembly",
+ "Final trimming of cardboard",
+ "Finalize wiping and placement of ketchup bottle",
+ "Finalizing and releasing folded paper",
+ "Finalizing cuts and storing tools",
+ "Finalizing item placement into shopping bag",
+ "Finalizing marks on cardboard",
+ "Finalizing paper star",
+ "Finalizing shelf organization",
+ "Finalizing shelf placement",
+ "Finalizing shelf placement and moving bin",
+ "Finalizing the craft assembly",
+ "Fine-tuning bead placement",
+ "Finish and place origami star",
+ "Finish cutting along the marked line and reposition",
+ "Finishing coil and selecting new strip",
+ "Finishing cut and placing scissors down",
+ "Finishing cutting and switching to phone",
+ "Finishing segment and placing scissors",
+ "Fold and grasp lantern",
+ "Fold paper star",
+ "Fold paper strip",
+ "Fold paper strip into knot",
+ "Fold paper strip into lucky star",
+ "Fold paper strip into star",
+ "Folding and organizing paper strips",
+ "Folding and positioning cardboard",
+ "Folding and shaping ribbon",
+ "Folding and sorting paper stars",
+ "Folding and sorting paper stars while handling a water bottle",
+ "Folding cardboard",
+ "Folding cardboard and checking phone",
+ "Folding cardboard and handling utility knife",
+ "Folding cardboard and preparing marker",
+ "Folding cardboard edge",
+ "Folding lucky star",
+ "Folding paper strip",
+ "Folding paper strip into lucky star",
+ "Folding paper strips",
+ "Folding paper strips while using phone",
+ "Folding plastic strip",
+ "Folding purple paper strip",
+ "Folding purple ribbon",
+ "Form paper strip into a star",
+ "Forming quilled paper shapes",
+ "Forming ribbon knot",
+ "Gather and hold beads",
+ "Gathering and boxing inventory",
+ "Gathering and boxing plastic pieces",
+ "Gathering cardboard pieces",
+ "Gathering colored beads",
+ "Gathering items",
+ "Gathering materials and walking",
+ "Gathering star beads",
+ "Grasp lantern component",
+ "Grasp paper strip",
+ "Grasping and placing products on shelf",
+ "Greeting participants",
+ "Guiding utility knife along ruler",
+ "Handle and prepare paper coil",
+ "Handle container from box",
+ "Handle crate of cans",
+ "Handle paper lantern component",
+ "Handle power bank and cable",
+ "Handling and organizing containers",
+ "Handling charging cables",
+ "Handling container lid",
+ "Handling earbud case",
+ "Handling electronic device",
+ "Handling miscellaneous items",
+ "Handling plastic strip",
+ "Handling shipping box",
+ "Handling snack packages",
+ "Hang shopping bag and exit area",
+ "Hold and carry tray of canned goods",
+ "Hold and cut newspaper with scissors",
+ "Hold and mark cardboard piece",
+ "Hold blue product box",
+ "Hold instructional sign",
+ "Hold newspaper",
+ "Hold paper strip",
+ "Hold quilling paper",
+ "Hold small object",
+ "Hold small white box",
+ "Holding a smartphone",
+ "Holding and adjusting cardboard",
+ "Holding and aligning paper strip",
+ "Holding and bagging a smartphone box",
+ "Holding and creasing purple paper",
+ "Holding and grasping product bags",
+ "Holding and inspecting product",
+ "Holding and organizing product packages",
+ "Holding and retrieving paper strips",
+ "Holding and rotating paper strip",
+ "Holding cardboard pieces",
+ "Holding cardboard with ruler",
+ "Holding container of canned food",
+ "Holding pen and paper",
+ "Holding smartphone",
+ "Holding writing materials",
+ "Inflate paper star",
+ "Initiating assembly",
+ "Inspect product",
+ "Inspect shelf condition",
+ "Inspect shelf condition and observe surroundings",
+ "Inspecting Dior gift box",
+ "Inspecting almond package and scanning shelves",
+ "Inspecting and approaching shelf",
+ "Inspecting and bagging a smartphone box",
+ "Inspecting and folding cardboard pieces",
+ "Inspecting and packing supplement bottle",
+ "Inspecting and placing cans on shelf",
+ "Inspecting and placing cardboard strips",
+ "Inspecting and stocking shelf items",
+ "Inspecting bottle",
+ "Inspecting cardboard",
+ "Inspecting pieces and switching to scissors",
+ "Inspecting shelf contents",
+ "Inspecting supplement bottle",
+ "Inspecting the cleaned plastic jar",
+ "Install display hooks",
+ "Interacting with coworker",
+ "Interacting with phone",
+ "Interlocking the craft strips",
+ "Interruption: handling charging case",
+ "Labeling and organizing cardboard squares",
+ "Labeling and placing cardboard square",
+ "Labeling and placing cardboard squares",
+ "Labeling and retrieving cardboard pieces",
+ "Labeling and switching markers",
+ "Labeling cardboard pieces",
+ "Labeling cardboard square",
+ "Labeling cardboard squares",
+ "Leaving the room and retrieving object",
+ "Managing shopping container",
+ "Manipulate adhesive strip",
+ "Manipulate and inspect colorful pieces",
+ "Manipulate and release paper strips",
+ "Manipulate colorful pieces",
+ "Manipulate craft pieces",
+ "Manipulate light blue strip",
+ "Manipulate paper decoration",
+ "Manipulate paper edge",
+ "Manipulate paper piece",
+ "Manipulate paper strip",
+ "Manipulate paper strips",
+ "Manipulate puzzle piece",
+ "Manipulate puzzle pieces",
+ "Manipulate quilled paper strip",
+ "Manipulate small paper segment",
+ "Manipulate star and prepare next strip",
+ "Manipulating and placing paper shapes",
+ "Manipulating and placing strip components",
+ "Manipulating and releasing paper strips",
+ "Manipulating and releasing quilling strips",
+ "Manipulating beads",
+ "Manipulating cardboard and picking up scissors",
+ "Manipulating cardboard piece",
+ "Manipulating cardboard shapes",
+ "Manipulating components on a strip",
+ "Manipulating paper quilling piece",
+ "Manipulating paper star",
+ "Manipulating paper stars",
+ "Manipulating paper strip",
+ "Manipulating paper strips",
+ "Manipulating plastic strip",
+ "Manipulating puzzle pieces",
+ "Manipulating quilled paper",
+ "Manipulating ribbon piece",
+ "Manipulating small object",
+ "Manipulating tools and quilling materials",
+ "Manipulating yellow strips",
+ "Mark cardboard for cutting",
+ "Mark cardboard with marker",
+ "Mark fabric",
+ "Mark fabric and reposition ruler",
+ "Mark fabric with pen and remove ruler",
+ "Mark fabric with pen and ruler",
+ "Mark lines on cardboard",
+ "Marking and cutting cardboard",
+ "Marking and positioning cardboard",
+ "Marking cardboard",
+ "Marking cardboard and preparing to cut",
+ "Marking cardboard measurements",
+ "Marking cardboard piece",
+ "Marking cardboard piece and preparing workspace",
+ "Marking cardboard pieces",
+ "Marking cardboard squares",
+ "Marking cardboard with pen",
+ "Marking dimensions on cardboard",
+ "Marking grid lines",
+ "Marking guidelines on cardboard",
+ "Marking lines and repositioning ruler",
+ "Marking lines on cardboard",
+ "Marking lines with pen",
+ "Marking list and moving blue beads",
+ "Marking list and sorting beads",
+ "Marking paper and adjusting piles",
+ "Measure and mark cardboard with ruler",
+ "Measuring and adjusting workspace",
+ "Measuring and marking cardboard",
+ "Measuring and marking cardboard for crafting",
+ "Measuring and marking cardboard for cutting",
+ "Measuring and marking cardboard with ruler",
+ "Monitoring task progress via smartwatch",
+ "Move product towards shelf",
+ "Move to stocking area",
+ "Move towards shelf and position tray",
+ "Moving along the aisle",
+ "Moving along the aisle to assess stock",
+ "Moving along the shelves",
+ "Moving and adjusting the vacuum cleaner",
+ "Moving around the kitchen",
+ "Moving away from and returning to the table",
+ "Moving away from workstation",
+ "Moving cardboard cutouts",
+ "Moving cardboard pieces across the workspace",
+ "Moving container and stocking items",
+ "Moving hand over pile and sorting orange buttons",
+ "Moving orange buttons",
+ "Moving orange buttons and interacting with smartphone",
+ "Moving origami stars",
+ "Moving plastic storage bin",
+ "Moving the vacuum cleaner",
+ "Moving through the room",
+ "Moving through workspace",
+ "Moving to workspace",
+ "Moving towards aisle",
+ "Moving utility knife along ruler",
+ "Moving, placing, and adjusting puzzle pieces",
+ "Navigating store and browsing shelves",
+ "Observe puzzle progress",
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+ "Observe workspace and reach for beads",
+ "Observing and pausing",
+ "Observing restocking needs",
+ "Observing shelf and relocating to next aisle",
+ "Observing workspace",
+ "Opening cardboard box",
+ "Operate and release smartphone",
+ "Organize and count beads",
+ "Organize inventory and reach for products",
+ "Organizing buttons into patterns",
+ "Organizing canned goods in container",
+ "Organizing cans into a storage box",
+ "Organizing cardboard pieces",
+ "Organizing container contents",
+ "Organizing paper pieces into piles",
+ "Organizing paper strips",
+ "Organizing pickle jars and maintaining cleaning tools",
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+ "Organizing snack pouches into containers",
+ "Organizing snacks in box",
+ "Organizing stars into a row",
+ "Organizing tools and materials",
+ "Pack beads into box",
+ "Paper quilling craft",
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+ "Performing precision cuts on cardboard",
+ "Pick up and begin folding paper strip",
+ "Pick up and deposit beads into box",
+ "Pick up and inspect light blue strip",
+ "Pick up and place accessory on shelf",
+ "Pick up and place buttons",
+ "Pick up and place canned goods on shelf",
+ "Pick up and place product on shelf",
+ "Pick up and place puzzle piece",
+ "Pick up and place star-shaped beads",
+ "Pick up and place tiles on stack",
+ "Pick up cereal boxes",
+ "Pick up electronic accessory from box",
+ "Pick up grocery item",
+ "Pick up product from box",
+ "Pick, place, and count beads",
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+ "Picking and placing items from bin onto shelves",
+ "Picking and stocking items from container",
+ "Picking up and bagging a charging cable",
+ "Picking up and bagging an electronic item",
+ "Picking up and packing products",
+ "Picking up and placing canned goods",
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+ "Picking up canned goods",
+ "Picking up knife and cutting cardboard",
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+ "Place canned food on shelf",
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+ "Place grocery item on shelf",
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+ "Placing cans onto the shelf",
+ "Placing cardboard piece",
+ "Placing container on floor",
+ "Placing containers on the shelf",
+ "Placing down scissors",
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+ "Placing items and returning to box",
+ "Placing items and transitioning",
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+ "Placing items on shelf",
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+ "Placing items on shelf and retrieving new items",
+ "Placing items on the shelf",
+ "Placing jar on shelf",
+ "Placing product bags on shelf",
+ "Placing products on shelf",
+ "Placing ribbon onto project",
+ "Placing snack packages in box",
+ "Placing snack packages on shelf",
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+ "Position tray and reach for beads",
+ "Position utility knife and begin cutting",
+ "Positioning and cutting cardboard",
+ "Positioning and cutting cardboard piece",
+ "Positioning cardboard piece",
+ "Positioning cardboard pieces",
+ "Positioning container near the shelf",
+ "Positioning newspaper and scissors",
+ "Positioning paper strip",
+ "Positioning puzzle piece",
+ "Positioning ribbon piece",
+ "Positioning ruler and drawing lines",
+ "Positioning ruler for cutting",
+ "Positioning ruler for measurement",
+ "Positioning scissors to cut cardboard",
+ "Positioning shelving dividers",
+ "Positioning the container on the floor",
+ "Positioning utility knife",
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+ "Prepare for further marking",
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+ "Prepare to cut cardboard",
+ "Prepare tools for marking",
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+ "Preparing container on shelf",
+ "Preparing craft area",
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+ "Preparing materials",
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+ "Preparing shopping bag",
+ "Preparing to organize container bin",
+ "Preparing tools and materials",
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+ "Preparing workspace for craft activity",
+ "Preparing workstation",
+ "Pushing the vacuum cleaner",
+ "Reach for and adjust Mahjong tiles",
+ "Reach for items in box",
+ "Reach for, pick up, and attempt to fit puzzle piece",
+ "Reaching for and adjusting shelf containers",
+ "Reaching for and picking up stars",
+ "Reaching for products",
+ "Reaching for products and preparing the shelf",
+ "Reaching for utility knife",
+ "Reaching into the box for more stock",
+ "Rearrange Mahjong tile",
+ "Rearrange Mahjong tiles",
+ "Rearranging containers on the shelf",
+ "Rearranging items from shelf back to box",
+ "Recording star count",
+ "Refining bead arrangement with marker",
+ "Refining button layout",
+ "Refining cardboard cuts",
+ "Release and adjust puzzle piece",
+ "Release and prepare new strip",
+ "Release paper strip",
+ "Release scissors and operate smartphone",
+ "Releasing paper and reaching for phone",
+ "Releasing scissors",
+ "Relocating storage bins along the aisle",
+ "Remove packaging and prepare component",
+ "Remove tools after marking",
+ "Removing and discarding damaged items",
+ "Removing items from bag and stocking them",
+ "Removing labels and moving along the shelf",
+ "Removing old shelf labels",
+ "Reorganize items in box",
+ "Reorganizing stock on shelf",
+ "Replenishing shelf stock",
+ "Reposition and cut cardboard",
+ "Reposition and cut newspaper",
+ "Reposition ruler and draw lines",
+ "Reposition sign and organize beads",
+ "Repositioning and cutting cardboard pieces",
+ "Repositioning stool for the next section",
+ "Restocking pineapple chips",
+ "Resuming cardboard cutting",
+ "Resuming cutting",
+ "Resuming cutting cardboard",
+ "Resuming recording star count",
+ "Resuming sorting paper stars",
+ "Retrieve and move cardboard tray",
+ "Retrieve and transport cereal",
+ "Retrieve food items from boxes",
+ "Retrieve product from box",
+ "Retrieve snack packs",
+ "Retrieving additional supplies",
+ "Retrieving and carrying containers",
+ "Retrieving and examining items from bag",
+ "Retrieving and placing canned goods",
+ "Retrieving and shelving container",
+ "Retrieving cleaning supplies",
+ "Retrieving items for bag placement",
+ "Retrieving items from bag",
+ "Retrieving items from boxes",
+ "Retrieving items from shelf",
+ "Retrieving materials",
+ "Retrieving next product",
+ "Retrieving plastic container and moving to aisle",
+ "Retrieving smartphone",
+ "Retrieving tools from bag",
+ "Returning Dior gift box to shelf",
+ "Returning canned goods to shelf",
+ "Returning to desk",
+ "Returning to desk and setting up smartphone",
+ "Returning to table and placing controller",
+ "Returning to the workspace",
+ "Returning to work table",
+ "Returning to workspace",
+ "Returning to workstation",
+ "Reviewing and organizing records",
+ "Reviewing craft instructions",
+ "Reviewing new product labels",
+ "Roll quilling paper",
+ "Rolling paper strips into coils",
+ "Scanning workspace",
+ "Scoring and cutting cardboard",
+ "Scrolling and placing smartphone down",
+ "Scrolling and setting down smartphone",
+ "Scrolling and tapping on smartphone",
+ "Scrolling and viewing content on phone",
+ "Scrolling on smartphone",
+ "Scrolling smartphone screen",
+ "Search for and pick up puzzle piece",
+ "Secure lantern with adhesive",
+ "Secure paper edges with adhesive",
+ "Securing ribbon with needle",
+ "Select and handle paper strips",
+ "Selecting a bottle",
+ "Selecting and bagging electronic accessories",
+ "Selecting and collecting bottled goods",
+ "Selecting and collecting oil bottles",
+ "Selecting and evaluating items",
+ "Selecting and manipulating paper strips",
+ "Selecting and packing spice jars",
+ "Selecting and picking up canned goods",
+ "Setting down water and picking up phone",
+ "Setting up smartphone",
+ "Sliding utility knife along ruler",
+ "Sort Mahjong tiles",
+ "Sort and adjust button line",
+ "Sort and arrange buttons",
+ "Sort and combine bead piles",
+ "Sort and group beads",
+ "Sort and place buttons",
+ "Sort and record bead counts",
+ "Sort beads",
+ "Sort beads and adjust phone",
+ "Sort beads and adjust tray position",
+ "Sort beads and record count",
+ "Sort beads by color",
+ "Sort beads by hand",
+ "Sort beads on table",
+ "Sort beads on the table",
+ "Sort buttons",
+ "Sort buttons by color",
+ "Sort canned goods in tray",
+ "Sort colorful paper pieces",
+ "Sort colorful pieces",
+ "Sort craft materials into piles",
+ "Sort purple beads",
+ "Sort puzzle pieces",
+ "Sort small colorful pieces",
+ "Sort small craft pieces",
+ "Sort star-shaped beads",
+ "Sorting and collecting cardboard squares",
+ "Sorting and counting beads",
+ "Sorting and grouping buttons",
+ "Sorting and processing cardboard strips",
+ "Sorting and reaching for pieces",
+ "Sorting and reaching for plastic pieces",
+ "Sorting and stacking cardboard pieces",
+ "Sorting beads by color",
+ "Sorting blue beads and marking list",
+ "Sorting blue beads and writing on paper",
+ "Sorting buttons",
+ "Sorting buttons by color",
+ "Sorting cardboard pieces",
+ "Sorting cardboard shapes and holding marker",
+ "Sorting colorful paper stars",
+ "Sorting gift boxes into a bin",
+ "Sorting light blue origami stars",
+ "Sorting orange button",
+ "Sorting orange buttons",
+ "Sorting origami stars",
+ "Sorting origami stars by color",
+ "Sorting pieces and retrieving smartphone",
+ "Sorting pieces and writing on paper",
+ "Sorting purple star-shaped objects",
+ "Sorting quilled paper pieces",
+ "Sorting small colored tiles",
+ "Sorting small star-shaped plastic pieces",
+ "Sorting squares and preparing for next cut",
+ "Sorting star-shaped objects",
+ "Sorting star-shaped objects by color",
+ "Sorting tiles by color",
+ "Sorting yellow star-shaped objects",
+ "Stabilizing cardboard for cutting",
+ "Stacking and organizing cardboard",
+ "Stacking cardboard pieces",
+ "Stacking cardboard squares",
+ "Steadying the ruler and pen",
+ "Stock multiple products on shelf",
+ "Stocking and repositioning box",
+ "Stocking canned goods",
+ "Stocking canned goods onto the shelf",
+ "Stocking containers on the shelf",
+ "Stocking gift boxes on the shelf",
+ "Stocking items and carrying container",
+ "Stocking items and reaching for container",
+ "Stocking jars on shelf",
+ "Stocking miscellaneous products on shelf",
+ "Stocking multiple cans on the shelf",
+ "Stocking product boxes",
+ "Stocking products and reorganizing bin",
+ "Stocking products from bin onto shelf",
+ "Stocking products on shelf",
+ "Stocking sauce bottles on shelf",
+ "Stocking snack packets on the shelf",
+ "Stocking snack pouches on the shelf",
+ "Stop measuring and transition to smartphone usage",
+ "Stopping sorting activity",
+ "Sweeping floor debris",
+ "Taking a break to drink water and check phone",
+ "Tapping and putting away smartphone",
+ "Tear newspaper",
+ "Tearing and preparing blue foam pieces",
+ "Tidying workspace",
+ "Touch pieces in box and interact with colleagues",
+ "Touching canned goods",
+ "Trace and remove pattern",
+ "Trace and remove pattern piece",
+ "Transferring items from shelf to shopping bag",
+ "Transferring products from box to shelf",
+ "Transition to new product selection",
+ "Transitioning and observing workspace",
+ "Transitioning from jar to tin can",
+ "Transitioning from utility knife to scissors",
+ "Transitioning to cutting",
+ "Transitioning to new workstation",
+ "Transitioning to smartphone usage",
+ "Transport cereal to shelf",
+ "Transport pasta to shelf",
+ "Transporting cardboard to collection area",
+ "Transporting snack packages to shelf",
+ "Trimming and placing cardboard pieces",
+ "Trimming and stacking cardboard pieces",
+ "Trimming cardboard piece",
+ "Typing and navigating on phone",
+ "Typing message on smartphone",
+ "Typing on smartphone",
+ "Typing on smartphone while working with paper",
+ "Unfold paper lantern",
+ "Unpack additional lantern component",
+ "Unpack and place items on shelf",
+ "Use smartphone",
+ "Using a smartphone",
+ "Using and placing phone",
+ "Using phone",
+ "Using phone and drinking water",
+ "Using phone and resuming work",
+ "Using smartphone",
+ "Using smartphone as a guide on cardboard",
+ "Vacuuming along the wall edge",
+ "Vacuuming the carpet",
+ "Vacuuming the carpet corner",
+ "Vacuuming the carpet edge",
+ "Walk to and approach packing area",
+ "Walk to shelf location",
+ "Walking along the aisle",
+ "Walking in workspace",
+ "Walking through office",
+ "Walking through the room",
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+ "Walking to storage area",
+ "Walking to the crafting area",
+ "Walking to workspace",
+ "Walking to workstation",
+ "Walking towards the desk",
+ "Walking towards workstation",
+ "Washing hands",
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+ "Wipe and select ketchup bottle",
+ "Wipe and transport product to shelf",
+ "Wipe shelf and pick up product",
+ "Wipe shelf and retrieve canned food",
+ "Wiping and cleaning retail items",
+ "Wiping and organizing grocery products",
+ "Wiping counter",
+ "Wiping product jars and shelf stocking",
+ "Wiping shelves",
+ "Wiping shelves and cleaning product items",
+ "Wiping shelves and picking up products",
+ "Wiping the plastic jar",
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+ "Write on paper",
+ "Writing on paper and reaching for beads",
+ "folding paper star",
+ "folding paper stars and typing on smartphone",
+ "folding paper strip",
+ "folding paper strips and retrieving stapler",
+ "folding paper strips into stars",
+ "manipulating paper star and using smartphone",
+ "manipulating paper strip",
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+ "episode_path": "/modelscope_data/xperience10m_128/b1292f20-bfca-497b-a41a-e7fd62bbf913/ep2",
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+ "episode_path": "/modelscope_data/xperience10m_128/cba9c19e-a55f-46e8-bda8-423cdcc2de5f/ep1",
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+ "episode_path": "/modelscope_data/xperience10m_128/9dc8fc7c-977f-444a-9331-06d2dd7bf120/ep2",
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+ "reason": "No labeled windows were created. Try lowering --min-label-fraction."
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+ "episode_path": "/modelscope_data/xperience10m_128/5aeb0920-ab9f-4dc2-a261-747a678bf9cb/ep2",
+ "reason": "No labeled windows were created. Try lowering --min-label-fraction."
+ },
+ {
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+ "episode_path": "/modelscope_data/xperience10m_128/480cb308-6ad0-4791-8bb6-f029f439548a/ep5",
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+ "episode_path": "/modelscope_data/xperience10m_128/acb1b145-804e-41fe-915e-16ff7f59433a/ep7",
+ "reason": "No labeled windows were created. Try lowering --min-label-fraction."
+ }
+ ],
+ "notes": [
+ "Shard media and sensor-feature paths remain in shard output directories.",
+ "Assistant answers are strict JSON for episode understanding, not robot-control policies.",
+ "Merged label options are recomputed globally across all shards.",
+ "Episodes with no labeled windows under the configured label rule are skipped and reported."
+ ]
+}
\ No newline at end of file
diff --git a/results/omni_finetune/verified_public/xperience10m_qwen3_omni_128ep_96train_16val_16test_valmon_20260605_eval/dataset/episode_manifest.json b/results/omni_finetune/verified_public/xperience10m_qwen3_omni_128ep_96train_16val_16test_valmon_20260605_eval/dataset/episode_manifest.json
new file mode 100644
index 0000000000000000000000000000000000000000..7943da1935ad5a8673550c0d1c0a3a9705339938
--- /dev/null
+++ b/results/omni_finetune/verified_public/xperience10m_qwen3_omni_128ep_96train_16val_16test_valmon_20260605_eval/dataset/episode_manifest.json
@@ -0,0 +1,22333 @@
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