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metadata 
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

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

Minimal task architectures