README.md

---
license: other
pretty_name: Ropedia Xperience-10M Task Suite Artifacts
tags:
  - robotics
  - embodied-ai
  - multimodal
  - ropedia
  - xperience-10m
  - evaluation
  - baseline
  - neural-network
  - pytorch
  - retrieval
task_categories:
  - robotics
  - time-series-forecasting
  - text-retrieval
language:
  - en
size_categories:
  - n<1K
---

# Ropedia Xperience-10M Task Suite Artifacts

This dataset repo contains the derived evidence layer for the public Xperience-10M sample episode released by Ropedia: metrics, predictions, manifests, charts, diagrams, notes, reproduction scripts, and the small neural MLP task-head artifacts.

![12-task infographic](assets/task_suite_infographic.png?v=xperience10m-taskfirst-v12-modality-xl)

The dashboard assets follow a Ropedia-inspired visual system: dark 4D-world
canvas, lime-green accents, point-cloud texture, thin green cards, and
research-grade typography, while all labels and metrics are script-generated
from committed result files.

The Space starts with a task-first 12-task map, then includes a native
responsive modality atlas backed by
`docs/data/modality_atlas.json` and `docs/assets/modalities/`, so each
public-sample stream remains readable on mobile without shipping raw videos or
annotations.

It does **not** contain raw Xperience-10M videos or raw `annotation.hdf5`. Download raw data only from the official Ropedia / Hugging Face sources and follow their terms.

Current scale-up status: the full `ropedia-ai/xperience-10m` Hugging Face
dataset is still gated for this account. The A100 relay has been configured to
poll access, download a 32-episode stratified pilot subset after approval,
validate it, transfer it to H20, and run the readiness gate. Until that
completes, the committed Qwen3-Omni artifacts remain smoke/debug evidence, not
real 32-episode held-out metrics.

## Why This Repo Exists

This is the reviewable half of the project. You can inspect the task outputs, compare the committed metrics, and understand the single-episode limitations without downloading the raw videos first.

## 90-Second Reviewer Path

| Step | Question | Primary artifacts |
| --- | --- | --- |
| 1 | What is actually claimed? | `EVIDENCE_CONTRACT.md`, `ARTIFACT_GUIDE.md`, `QUALITY_GATES.md`, `docs/data/evidence_contract.json`, `docs/data/artifact_index.json`, `docs/data/live_publication_status.json`, `docs/data/quality_gates.json`, `docs/data/mirror_parity.json`, `docs/data/scope_claims_audit.json`, `docs/data/publication_audit.json`, `docs/data/website_integrity.json` |
| 2 | How do I reproduce it? | `REPRODUCIBILITY.md`, `docs/data/reproducibility_matrix.json`, `notes/reproducibility_audit.md` |
| 3 | What is one model input? | `results/episode_task_suite/windows.csv`, `results/episode_task_suite/feature_manifest.json`, `results/episode_task_suite/available_modalities.json` |
| 4 | Are the task results backed by files? | `results/episode_task_suite/summary_report.json`, `results/episode_task_suite/neural_mlp/`, `docs/data/summary_metrics.json` |
| 5 | What is still pending? | `results/omni_finetune/DATA_BLOCKER_REPORT.md`, `results/omni_finetune/A100_HF_RELAY_STATUS.md`, `scripts/omni/discover_xperience10m_sources.py` |

Human-readable artifact guide: `ARTIFACT_GUIDE.md`.
Publication quality gates: `QUALITY_GATES.md` and `docs/data/quality_gates.json`.
Live publication status: `docs/data/live_publication_status.json`.
Machine-readable reviewer packet: `docs/data/reviewer_packet.json`.
Source-of-truth artifact index: `docs/data/artifact_index.json`.

## Evidence Contract

| Claim layer | Evidence | Boundary |
| --- | --- | --- |
| Data windows | `results/episode_task_suite/windows.csv`, `shared_windows.npz`, `summary_report.json` | one public sample episode |
| Feature contract | `results/episode_task_suite/feature_manifest.json`, `available_modalities.json` | 8,378 current features; audio documented but not featurized |
| 12-task suite | per-task `metrics.json`, predictions, confusion matrices | chronological single-episode split |
| Neural heads | `results/episode_task_suite/neural_mlp/` | compact MLP heads, not a foundation model |
| Research directions | `research_direction_taxonomy.json`, extension probe results | direct/proxy/diagnostic evidence, not full solutions |
| Qwen3-Omni | `DATA_BLOCKER_REPORT.md`, `A100_HF_RELAY_STATUS.md` | smoke-only until 32 valid episodes are available |
| Scope claims guard | `docs/data/scope_claims_audit.json`, `scripts/validate_scope_claims.py` | historical `32ep` path strings are provenance, not 32-episode results |
| Mirror parity | `docs/data/mirror_parity.json`, `scripts/validate_mirror_parity.py` | prepared repo/HF mirrors carry matching critical data, figures, website HTML, and validator files |
| Publication hygiene | `docs/data/publication_audit.json`, `scripts/validate_publication_package.py` | public files/HF bundles only, with public-card freshness checks |
| Website integrity | `docs/data/website_integrity.json`, `scripts/validate_website_integrity.py` | local links, anchors, JSON bundles, and referenced images only |
| Quality gates | `QUALITY_GATES.md`, `docs/data/quality_gates.json`, `scripts/build_quality_gates.py` | automated release gates plus live post-publish checks |
| Live publication | `docs/data/live_publication_status.json`, `scripts/verify_live_publication.py` | last public GitHub/HF URL verification after upload |
| Artifact index | `docs/data/artifact_index.json`, `scripts/build_artifact_index.py` | compact proof-artifact catalog with stable hashes |
| Citation metadata | `PROJECT_README.md`, `docs/data/project_manifest.json`, GitHub `CITATION.cff` | code/data license boundary remains explicit |

## What Is Included

- `ARTIFACT_GUIDE.md`: human-readable map of proof boundary, data contract, task evidence, platform mirrors, and scale-up status
- `REPRODUCIBILITY.md` and `docs/data/reproducibility_matrix.json`: public commands, expected outputs, exact-match audit evidence, and non-reproducible boundaries
- `results/**/*.json`: verified metrics and metadata for minimal and neural MLP runs
- `results/**/*.csv`: predictions, confusion matrices, per-class metrics, windows, boundaries
- `results/**/history.json`: neural MLP training traces
- `docs/assets/*.svg` and `docs/assets/*.png`: generated diagrams, charts, and ChatGPT-image-backed overview figures
- `docs/assets/task_suite_infographic.png`: ChatGPT-image-backed infographic with the shared processing contract, all 12 task families, verified metric overlays, and enlarged public-sample modality thumbnails below the task map
- `docs/assets/modalities/` and `docs/data/modality_atlas.json`: small derived sample thumbnails and metadata for the responsive modality atlas
- `docs/data/summary_metrics.json`: dashboard-readable summary bundle
- `docs/data/evidence_contract.json`: machine-readable proof boundary
- `docs/data/artifact_index.json`: source-of-truth proof-artifact catalog with stable-file hashes
- `docs/data/mirror_parity.json`: prepared Space/artifact/model mirror parity check, including critical website HTML
- `docs/data/scope_claims_audit.json`: machine-readable guard against overclaiming historical `32ep` smoke-run identifiers
- `docs/data/publication_audit.json`: machine-readable publication hygiene and public-card freshness check
- `docs/data/website_integrity.json`: machine-readable website local-reference integrity check
- `QUALITY_GATES.md` and `docs/data/quality_gates.json`: reviewer-facing and machine-readable release gates
- `docs/data/live_publication_status.json`: last live public URL verification after upload
- `docs/data/project_manifest.json`: machine-readable public URL and citation metadata
- `docs/data/reviewer_packet.json`: machine-readable reviewer path and proof boundary
- `docs/data/research_directions.json`: generated four-track taxonomy for the website
- `docs/data/research_direction_extensions.json`: four extra data-backed probes, one per research direction
- `docs/data/task_walkthroughs.json`: beginner-oriented input/process/output guide for all 12 tasks
- `results/episode_task_suite/research_directions/`: JSON, CSV, and Markdown task-to-research-track mapping
- `results/episode_task_suite/research_direction_extensions/`: metrics, prediction CSVs, rank CSVs, and Markdown summary for the four extension probes
- `results/episode_task_suite/task_walkthroughs/`: case-study walkthroughs for every task contract
- `scripts/*.py`: reproduction scripts
- `scripts/export_modality_atlas_assets.py`: regenerates the responsive modality-card thumbnails and manifest from the local public sample
- `scripts/build_artifact_index.py`: source-of-truth artifact-index builder
- `scripts/validate_mirror_parity.py`: prepared mirror parity validator
- `scripts/validate_scope_claims.py`: validates the Qwen3-Omni smoke/result claim boundary
- `scripts/validate_publication_package.py`: public bundle validator
- `scripts/validate_website_integrity.py`: website local-reference validator
- `notes/*.md`: interpretation and reproducibility notes

The companion model repo stores the lightweight model checkpoints and mirrors
the binary arrays (`model.npz`, `model.pt`, and compact neural prediction
arrays). This artifact dataset stays focused on reviewable CSV/JSON/Markdown,
scripts, notes, and visual assets:

https://huggingface.co/cy0307/ropedia-xperience-10m-task-baselines

## Links

| Resource | URL |
| --- | --- |
| Hugging Face Space | https://huggingface.co/spaces/cy0307/ropedia-xperience-10m-task-suite |
| Live Hugging Face app | https://cy0307-ropedia-xperience-10m-task-suite.static.hf.space/ |
| Hugging Face collection | https://huggingface.co/collections/cy0307/ropedia-xperience-10m-task-suite |
| Minimal and neural task baseline repo | https://huggingface.co/cy0307/ropedia-xperience-10m-task-baselines |
| GitHub repo | https://github.com/ChaoYue0307/ropedia-xperience-10m-task-suite |
| GitHub Pages dashboard | https://chaoyue0307.github.io/ropedia-xperience-10m-task-suite/ |
| Xperience-10M website | https://ropedia.com/dataset |
| Xperience-10M release page | https://ropedia.com/blog/20260316_xperience_10m |
| Ropedia GitHub organization | https://github.com/Ropedia |
| HOMIE Toolkit | https://github.com/Ropedia/HOMIE-toolkit |
| Xperience-10M dataset | https://huggingface.co/datasets/ropedia-ai/xperience-10m |
| Xperience-10M sample | https://huggingface.co/datasets/ropedia-ai/xperience-10m-sample |

## Scope

The artifacts validate one public sample episode:

- 5,821 aligned frames
- 1,161 sliding windows
- 8,378 current feature dimensions, with audio documented but not featurized
- 12 supervised/self-supervised task definitions
- minimal linear/ridge baselines and neural MLP heads for all 12 tasks
- four direction-extension probes with minimal and neural MLP baselines
- chronological 70/30 split

For research claims, rerun the same scripts over many episodes and evaluate on held-out episodes.

## Neural MLP Result Snapshot

These are single-episode chronological-split metrics. They are useful for debugging task definitions and input contracts, not for claiming cross-episode generalization.

| Task | Neural metric | Minimal metric |
| --- | ---: | ---: |
| `timeline_action` macro-F1 | 0.0263 | 0.0500 |
| `timeline_subtask` macro-F1 | 0.0175 | 0.0495 |
| `transition_detection` macro-F1 | 0.6485 | 0.6552 |
| `next_action` macro-F1 | 0.0235 | 0.0593 |
| `hand_trajectory_forecast` MPJPE, lower is better | 0.1116 | 0.8223 |
| `contact_prediction` macro-F1 | 1.0000 | 1.0000 |
| `object_relevance` micro-F1 | 0.1798 | 0.1839 |
| `caption_grounding` MRR | 0.0178 | 0.0172 |
| `cross_modal_retrieval` MRR | 0.1530 | 0.2634 |
| `modality_reconstruction` R2 | -0.0102 | -0.0160 |
| `temporal_order` F1 | 0.8718 | 0.5487 |
| `misalignment_detection` F1 | 0.7335 | 0.4866 |

Primary NN artifact path:

`results/episode_task_suite/neural_mlp/<task>/`

## Four Research Directions

The current 12 tasks are organized into the four Ropedia research directions
with two baselines per task: minimal interpretable heads and neural MLP heads.

| Direction | Current status | Evidence |
| --- | --- | --- |
| A. Human Modeling & Motion Understanding | partially implemented | hand trajectory and contact are direct; action/object tasks are proxies |
| B. 3D/4D Reconstruction & Neural Rendering | proxy tasks only | retrieval, reconstruction, and misalignment diagnose prerequisites |
| C. Egocentric Vision & Interaction | strongest implemented track | 6 direct tasks plus order/alignment diagnostics |
| D. Scene Reconstruction & World Modeling | early proxy tasks | state, object, retrieval, reconstruction, and temporal probes |

Primary taxonomy artifact:

`results/episode_task_suite/research_directions/research_direction_taxonomy.json`

## Four Direction-Extension Probes

The artifact bundle also includes one extra coded probe for each Ropedia
research direction. These are still single-episode diagnostics, but they make
the four-direction roadmap concrete.

| Direction | Extension task | Minimal | Neural MLP |
| --- | --- | ---: | ---: |
| A. Human Modeling & Motion Understanding | `body_motion_intensity` | 0.7827 macro-F1 | 0.7986 macro-F1 |
| B. 3D/4D Reconstruction & Neural Rendering | `multi_view_consistency_retrieval` | 0.5534 MRR | 0.3469 MRR |
| C. Egocentric Vision & Interaction | `action_phase_progress` | 0.3416 MAE | 0.3038 MAE |
| D. Scene Reconstruction & World Modeling | `ego_motion_forecast` | 0.1989 MAE | 0.0989 MAE |

Primary extension artifact:

`results/episode_task_suite/research_direction_extensions/research_direction_extension_results.json`

## Junior Task Walkthroughs

Each task has a case study, input contract, middle process modules, output
contract, metric, and current limitation. Start here when onboarding a junior
researcher or engineer:

`results/episode_task_suite/task_walkthroughs/TASK_WALKTHROUGHS.md`

## Pending 32-Episode Pilot

| Item | Value |
| --- | --- |
| Selection strategy | stratified round-robin across top-level session UUIDs |
| Candidate scan | first 64 top-level session UUIDs |
| Valid complete candidates | 680 |
| Selected pilot episodes | 32 from 32 session UUIDs |
| Estimated raw subset | about 72.0 GB |
| Excluded file type | `visualization.rrd` |
| Blocker | HF gated dataset approval pending |

![Verified episode pipeline](assets/pipeline_diagram.png)

![Minimal task architectures](assets/task_architectures.png)