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cy0307/
ropedia-xperience-10m-task-baselines

Name: ropedia-xperience-10m-task-baselines
Availability: InStock

pytorch

xperience10m_task_baseline_suite

ropedia-ai/xperience-10m-sample

ropedia-ai/xperience-10m

mit

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Ropedia Xperience-10M Task Suite cover

Ropedia Xperience-10M Task Suite

Ropedia Xperience-10M Task Suite logo

Public task and evaluation layer for Xperience-10M: sample data, 20 embodied-AI tasks, baselines, Qwen3-Omni and Cosmos3 diagnostics, and foundation-model training directions.

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This project builds on the Xperience-10M dataset released by Ropedia to provide public, reproducible embodied-AI evaluation materials. It is organized into two evidence lines. Line 1 turns one public sample episode into inspectable tasks, targets, and baseline runs. Line 2 uses selected 128-episode public-safe artifacts for aligned metadata/raw baselines, Qwen3-Omni v6 LoRA, Cosmos3-Super Reasoner, and Cosmos3-Nano Future Window. Every score links back to its source artifact, and direct scores remain clearly separated from compact-proxy estimates.

Updated: 2026-06-23.

Scope: Line 1 uses one public sample episode. Line 2 uses selected 128-episode public-safe artifacts linked back to official gated episode paths. Raw Xperience-10M MP4/HDF5/RRD files, Qwen3 base weights, Cosmos3 base weights, and gated data are not redistributed here.

Cite This Repository

Use this copyable BibTeX block for the repository:

bibtex

@misc{he2026ropedia_xperience10m_task_suite,
  author       = {Chaoyue He},
  title        = {Ropedia Xperience-10M Task Suite},
  year         = {2026},
  url          = {https://github.com/ChaoYue0307/ropedia-xperience-10m-task-suite},
  note         = {Public task and evaluation layer for Ropedia Xperience-10M: two evidence lines, 20 embodied-AI tasks, 180 scored method-task records, and selected-128 model diagnostics}
}

Project Entry Points
At A Glance
Data Explorer Analysis
Two Evidence Lines
Fast Project Map
Why This Project Exists
Start Here
Glossary
Current Research Scope
Evaluation Protocol
Dataset Context
Reproducibility
Citation / BibTeX

Project Entry Points

Use the two evidence lines first, then choose the artifact that answers your question. The dashboard is the best visual overview; the GitHub repo is the source of truth for scripts and generated JSON; Hugging Face mirrors contain public-safe cards, metrics, figures, and model artifacts.

Quick rule: use Line 1 for “can I inspect and reproduce the task?” Use Line 2 for “how do aligned baselines and model diagnostics compare on the selected 128 episodes?”

The committed task contracts, result matrices, validation JSON, public-safe result packages, GitHub sources, and Hugging Face mirrors are the canonical technical evidence.

At A Glance

Signal	Current public state
Project identity Ropedia Xperience-10M Task Suite logo	The same project logo mark is used across the GitHub README, GitHub Pages dashboard, Hugging Face Space, artifact dataset, model mirrors, favicon, and social preview. Ropedia is credited as the Xperience-10M data provider and releaser; this repository is the task-suite and evaluation layer built on that dataset. Reusable assets: logo mark and social card.
Two-line contract	Line 1: 1 sample episode for task construction and reproducibility. Line 2: 128 selected episodes for same-split metadata/raw baselines, Qwen3-Omni v6, and Cosmos3 diagnostics.
Data explorer analysis	A generated analysis layer separates the public sample, selected-128 feature exports, and authenticated Hugging Face gated full-dataset metadata with scope stats, split counts, modality breakdowns, and chart assets.
180 method-task records	9 methods x 20 tasks = 180/180 scored records. The ledger separates 174 direct scores from 6 compact-proxy scores.
20 task contracts	Action, procedure, transition, trajectory, contact, objects, language, retrieval, reconstruction, order, sync, long-horizon forecasting, interaction text, action-object binding, sensor bridging, camera sync, and transition timing.
4 research directions	Human Modeling & Motion Understanding; 3D/4D Reconstruction & Neural Rendering; Egocentric Vision & Interaction; Scene Reconstruction & World Modeling. These are analysis groups over the same 20 tasks, not separate benchmark tiers.
Line 1 methods	Minimal and Neural MLP baselines cover all 20 tasks on the one public sample episode: 40/40 direct scores.
Line 2 methods	Metadata simple/NN, raw-feature simple/NN, Qwen3-Omni v6 LoRA, Cosmos3-Super Reasoner, and Cosmos3-Nano Future Window cover all 20 selected-128 task axes: 140/140 scores.
3 foundation pipelines	Spatial intelligence, human-video world modeling, and vision-language-action pipelines are documented as training recipes with task mappings, input-output contracts, and model-evidence requirements.
1 unified target	The long-term embodied foundation-model target connects perception, 3D memory, language-grounded reasoning, action, and planning without adding a new score axis.
Public mirrors	GitHub, GitHub Pages, HF Space, HF artifact dataset, HF baseline model repo, Qwen3-Omni and Cosmos3 model repos, and HF collection.

Public Structure: 20 Tasks / 4 Directions / 3 Pipelines / 1 Unified Target

The project has four connected layers. The 20 tasks are the scored benchmark contracts. The 4 directions are research groupings over those same tasks. The 3 foundation pipelines are training recipes that reuse the same modalities, windows, and task targets. The 1 unified embodied model target is the long-term integration goal after those pipelines mature.

Layer rule: if it has a metric, it is a task; if it explains what the evidence studies, it is a direction; if it describes model inputs and training targets, it is a pipeline; if it combines perception, 3D memory, language, action, and planning, it is the unified target rather than an extra score axis.

Relationship map showing 20 task contracts, 4 research directions, 3 foundation-model pipeline tracks, and 1 unified embodied model target

Layer	Count	Role	Exact public labels
Task contracts	20	Score axes used by the matrix, radars, task cards, and method rows.	Action Recognition; Procedure Step Recognition; Action Boundary Detection; Next-Action Prediction; Hand Trajectory Forecasting; Contact State Prediction; Object Relevance Prediction; Language Grounding; Cross-Modal Retrieval; Cross-Modal Reconstruction; Temporal Order Verification; Multimodal Synchronization Detection; Long-Horizon Next-Action Forecasting; Long-Horizon Next-Subtask Forecasting; Interaction Text Prediction; Action-Object Relation Prediction; Future Object-Set Forecasting; IMU-to-Hand Pose Reconstruction; Camera-View Synchronization Retrieval; Time-to-Next-Transition Regression.
Research directions	4	Ways to interpret what the 20 tasks study; not separate benchmark tiers.	Human Modeling & Motion Understanding; 3D/4D Reconstruction & Neural Rendering; Egocentric Vision & Interaction; Scene Reconstruction & World Modeling.
Foundation pipelines	3	Larger-model training tracks with separate input-output recipes and result gates.	Spatial intelligence models; Human-video world models; Vision-language-action models.
Unified embodied model target	1	Long-term integration target, not a task/method row in the 180-result matrix.	Perception; 3D memory; language-grounded reasoning; action; planning.

Data Explorer Analysis

The data explorer is now a three-scope analysis layer, not only a raw-file browser. It compares the public sample episode, selected 128-episode feature exports, and the Hugging Face-hosted gated full-dataset metadata without mixing their evidence boundaries.

Scope	Question	Current public analysis
Public sample	What files and signals are directly inspectable?	1 episode, 5,821 frames, 1,161 aligned 20-frame windows, 8,546 feature dimensions, raw-file browser, modality breakdowns, action-window distribution.
Selected 128	What selected-episode surface supports model comparison?	96/16/16 split, 34,269 Qwen3-Omni v6 multiscale rows, 106,095 dense compact rows, selected episode links, public-safe matrices.
Full HF dataset	How large is the official upstream dataset?	Authenticated Hub file metadata: 804 sessions, 12,103 episode-like folders, 85,257 files, 24.63 TiB training-byte view, without redistributing raw gated data.

Entry points: website analysis section, analysis report, and structured analysis record.

Two Evidence Lines

The public suite is organized around two evidence lines. Keep them separate when comparing metrics.

Two evidence-line map: 1 sample episode and 128 selected episodes combine into 180 scored method-task records

Line	Data unit	Score statement	Best use	Read separately from
1 sample episode	One public Xperience-10M sample episode: 5,821 frames, 1,161 aligned 20-frame windows, 8,546 feature dimensions.	40/40 direct scores from Minimal and Neural MLP heads.	Inspect the raw sample, understand file organization, reproduce the 20 task targets, and compare Minimal vs Neural MLP behavior inside one episode.	The selected-128 comparison rows and any broader held-out model behavior.
128 selected episodes	Selected held-out 96/16/16 split: 34,269 exported windows with public-safe processed features linked to official gated episode paths. The Hugging Face artifact dataset exposes these rows separately as `selected_128_windows/selected_128`; it is not mixed with the one-sample `episode_sample/public_sample` viewer.	140/140 selected-128 scores: 134 direct + 6 compact-proxy.	Compare same-split metadata/raw baselines, Qwen3-Omni v6, Cosmos3-Super, and Cosmos3-Nano while keeping the 6 compact-proxy cells visible.	Direct raw-target measurements for the proxy-marked cells.

Result Ledger

Line	Methods	Tasks	Scored records	Direct scores	Proxy scores
1 sample episode	2	20	40/40	40	0
128 selected episodes	7	20	140/140	134	6 compact-proxy scores, each source-linked and reasoned.
Total public matrix	9	20	180/180	174	6

Method Blocks

Evidence line	Method block	Methods	Score statement	Read as
1 sample episode	Task-head baselines	Minimal; Neural MLP	40/40 direct scores.	Task-lab reproducibility and simple-vs-neural behavior.
128 selected episodes	Aligned baseline heads	Metadata simple/NN; raw-feature simple/NN	80/80 scores: 74 direct + 6 compact-proxy.	Same-split metadata/raw-feature baseline comparison.
128 selected episodes	Qwen3-Omni series	Qwen3-Omni v6 LoRA	20/20 direct scores from verified selected-128 Qwen3-Omni LoRA and task-specific probes.	Trainable Qwen3-Omni diagnostic baseline on the selected-128 surface.
128 selected episodes	Cosmos3 series	Cosmos3-Super Reasoner; Cosmos3-Nano Future Window	40/40 direct scores from verified public-safe reasoner and future-window artifacts.	Cosmos3 reasoner and future-window diagnostics on the selected-128 surface.

Cosmos3-Super Forward-Dynamics LoRA is published as a separate fine-tuned adapter artifact with weights/results; it is not counted as a 20-task matrix method row.

Qwen3-Omni Run Versions

These are Qwen3-Omni run versions inside Line 2: selected 128 episodes. They are not the project evidence lines. The 20-task matrix uses Qwen3-Omni v6 LoRA; v5 remains the pinned prior multiscale release; v1-v4 are lineage and ablation evidence.

Run	Purpose	Main change	Eval signal	Use now
v1	Prove the selected-128 LoRA/eval/package loop.	First verified 96/16/16 selected-episode Qwen3-Omni LoRA run.	448 eval; JSON 0.8750; contact 0.6451.	Lineage only.
v2	Make answers schema-checked.	Structured-JSON contract with full-8-GPU LoRA on the same split.	448 eval; JSON 0.9978; contact 0.7188.	Structured-output ablation.
v3	Separate prompt/eval effects from training.	Strict-label prompt/eval over the v2 adapter; no new adapter training.	448 eval; JSON 1.0000; contact 0.7210.	Prompt/eval ablation.
v4	Test longer structured-JSON LoRA training.	New four-epoch full-8-GPU adapter on the same selected split.	448 eval; JSON 1.0000; contact 0.7299.	Overfit/metric-tradeoff evidence.
v5	Move to denser multiscale evaluation.	Multiscale cap96 export with 4,032 held-out predictions.	4,032 eval; JSON 1.0000; contact 0.7865.	Pinned prior release; stronger on several non-contact metrics.
v6	Publish the current Qwen 20-task row.	Rank64/lr5e-5 multiscale LoRA plus verified task-specific probes.	4,032 eval; JSON 0.9990; contact 0.8177.	Current public 20-task Qwen3-Omni row.

Detailed lineage: QWEN3_OMNI_RUN_LINEAGE.md and qwen3_omni_run_lineage.json.

Result entry points: TWO_EVIDENCE_LINES.md, two_evidence_lines.json, TWO_EVIDENCE_LINE_RESULT_SUMMARY.md, two_evidence_line_result_summary.json, QWEN3_OMNI_RUN_LINEAGE.md, qwen3_omni_run_lineage.json, single_episode_task_model_radar.json, episode128_task_model_radar.json, task_method_20_result_matrix.json, and xperience10m_128_episode_feature_index.json.

Fast Project Map

Goal	Start here	Then inspect
Understand quickly	Project brief Project status	Dashboard
Choose the published mirror	Public evidence map	evidence-map data
Decode project terms	Glossary	glossary data
Inspect the 20 tasks	20-task guide	task contract data task walkthroughs
Explore data scales	Data explorer analysis	website analysis section structured analysis record
Compare results	Research takeaways	two-line result summary 180-record result table radar data score/proxy audit
Understand one sample	Single-episode explorer	sample-file map feature manifest
Read foundation directions	Three foundation pipelines	pipeline contract data foundation model plan
Reproduce or audit	Reproducibility Evidence contract	quality gates publication audit mirror parity

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, where the selected-128 comparison begins, and what evidence should exist before presenting stronger model quality.

The work is designed to demonstrate four capabilities that matter for embodied-AI research infrastructure:

Capability	What this project shows
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 20 human-readable tasks in one unified public-sample suite, 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 the sample readout from held-out comparison rows.
Scale-up planning	Connects the public-sample pipeline to 32/128-episode held-out pilots, Qwen3-Omni LoRA, Cosmos-style world-model tracks, policy/VLA tracks, and the future Xperience-native foundation-model pretraining goal.

Start Here

The public release is split across GitHub, the website, and Hugging Face. Use the public evidence map first for the shortest route through those mirrors, or use the machine-readable companion evidence-map data. For the one-page project summary, use the project brief and project-summary data.

Goal	Best entry point
Choose the right published mirror	Public evidence map evidence-map data
Resolve confusing terms and abbreviations	Glossary glossary data
Understand the whole project quickly	Project brief
See the visual research dashboard	GitHub Pages dashboard
Navigate the unified 20 tasks, four tracks, and scale-up plan	Interactive research roadmap 20-task suite note task contract data interactive roadmap data
Compare current task metrics	Research takeaways summary metrics
Compare possible foundation backbones	Foundation-model plan foundation-model data
Understand the future native pretraining goal	Native pretraining plan
See additional concrete project directions	Additional development directions direction data
Understand one model input	feature manifest window table
Check multi-episode data status	multi-episode data status

Glossary

Use the glossary when a term such as evidence line, 20-frame window, direct score, compact-proxy score, raw metric value, normalized radar value, minimal/minimum baseline, simple baseline, Qwen v1-v6, Cosmos3-Super, LoRA adapter, or HF artifact dataset is unclear. The same definitions are mirrored as glossary data for the website and Hugging Face repos.

Published Mirror Map

Surface	What it is for
GitHub repo	Source of truth for docs, scripts, generated data, validators, and commit history.
GitHub Pages dashboard	Best visual overview of the sample, 20 tasks, radar results, foundation directions, and resources.
Hugging Face Space	Hub-hosted copy of the dashboard and static app assets.
HF artifact dataset	Public-safe metrics, reports, website data, result packages, and derived evidence files.
HF baseline model repo	Minimal/neural baseline weights, figures, metrics, and mirrored task artifacts.
Qwen3-Omni and Cosmos3 model repos	Adapter-specific public weights or package cards when Qwen3-Omni v6, Cosmos3-Super, or Cosmos3-Nano runs are verified and publishable.

Public release checks are exposed as structured records for mirrors and dashboards: website integrity, rendered-site check, task-surface integrity, publication audit, mirror parity, public-surface QA, and roadmap data.

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	20 human-readable tasks form one embodied-AI public-sample suite with shared windowing, split discipline, leakage controls, and minimal/neural head pattern.
Baselines	Minimal linear/ridge/logistic heads plus compact PyTorch MLP task heads over the same chronological split; companion simple/NN metadata baselines are also aligned to the selected 128-episode 96/16/16 split.
Research directions	Task mapping and extension probes for human modeling, 3D/4D reconstruction, egocentric interaction, and world modeling.
Scale-up path	The selected-episode Qwen3-Omni LoRA v6 diagnostic package is verified on the 96/16/16 split with 34,269 exported windows and 4,032 held-out test predictions. v6 improves action macro-F1/contact accuracy versus v5; v5 remains a pinned prior-release row where it is stronger on other metrics. Same-split simple/NN metadata and raw-feature baselines are now reported on the unified 20-task axes, with compact-proxy notes retained where a target is derived from public-safe processed artifacts. The Qwen result proves the multi-episode export/train/eval/package loop and meets the strict-JSON target, but weak action/subtask metrics make it a baseline for error analysis rather than a strong model. Cosmos3 has three verified diagnostics: Nano future-window compatibility, Super base-weight Reasoner evaluation, and Super forward-dynamics LoRA fine-tuning over camera-pose proxy targets.
Published mirrors	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 the research takeaways and takeaway data. 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,
20 end-to-end episode-level task contracts,
one shared 20-frame window and chronological split contract across the public-sample task suite,
lightweight neural MLP heads for the same 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 20 tasks, four research tracks, current sample evidence, the Qwen3-Omni scale-up path, and foundation-model track 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_STATUS.md
Task suite	Twenty human-readable tasks cover recognition, prediction, retrieval, reconstruction, synchronization, long-horizon forecasting, interaction text, action-object binding, sensor bridging, camera sync, and transition timing. Historical `tier2_task_suite` artifact paths are kept for link stability, but they are provenance paths inside the same suite.	20-task suite note task contract data research takeaways summary report historical provenance baselines
Baselines	Minimal heads and compact PyTorch MLP heads provide a controlled single-episode comparison on the same chronological split. The selected 128-episode setup adds same-split metadata simple/NN baselines for JSON-supported tasks and raw-feature simple/NN baselines on all 20 task axes. Tasks 15 and 19 are explicitly marked as compact-proxy completions.	neural MLP outputs baseline alignment report raw20 run summary
Diagnostics	Audio contribution, modality ablations, timeline overlays, object labels, and alignment stress tests show which signals are useful and which tasks remain hard.	audio ablation summary single-episode explorer
Scale-up	Qwen3-Omni LoRA v6 is verified on the selected 96/16/16 split with 34,269 exported windows and 4,032 held-out test predictions. v6 improves action macro-F1/contact accuracy versus v5; v5 remains a pinned prior-release row because it is stronger on several other metrics. Same-split simple/NN metadata baselines are published for JSON-supported axes, and the raw-feature run adds simple/NN baselines on 20/20 task axes. Tasks 15 and 19 are documented compact proxies because raw interaction strings and paired video-view embeddings are absent from the 128 export. Cosmos3-Nano has a verified future-window compatibility package; Cosmos3-Super has a 448-window base-weight JSON-task Reasoner evaluation. Cosmos3-Super also has a fine-tuned forward-dynamics LoRA package over camera-pose proxy targets with 2,848 train rows, 512 validation rows, and 448 test rows. The 128-episode enhancement pack records dense-window sizing, hierarchical action/subtask targets, task bottlenecks, and next experiment cards without overwriting existing results.	research roadmap foundation-model plan selected-128 feature index note selected-128 feature-index data selected-128 enhancement note selected-128 enhancement data model comparison data verified Omni result data Qwen v5/v6 comparison data Qwen v5/v6 comparison note Omni model comparison note verified public package selected-128 enhancement run

Detailed dataset notes, reproduction checks, and generated data reports are included for implementation inspection. The full file map is available in the artifact guide.

Source alignment is tracked in the source-alignment note and source-alignment data. Whenever this repo says "full dataset," it refers to the Hugging Face-hosted gated dataset version at ropedia-ai/xperience-10m, not a local raw-data mirror. The official gated ropedia-ai/xperience-10m card reports 31.9 TB on the live Hugging Face dataset card and an about-1PB full-scale storage statement; the committed API-listing snapshot records 12,103 episode folders as upstream metadata only, not a local raw-data inventory. In other words, those episode folders are upstream listing metadata only for this project. The public sample remains ropedia-ai/xperience-10m-sample under cc-by-nc-4.0, with the HOMIE Toolkit and Rerun 0.29.0 noted as source tooling. The official responsible-use note that the data is limited in diversity is preserved.

Project Status

If you only have one minute, use the project status note and project-status data. 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.
20-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	Final verified diagnostic result package exists for the selected 96/16/16 episode split; JSON validity meets the target, while action/subtask metrics remain weak.
Raw data / full Qwen weights	Raw Xperience-10M data and full Qwen weights are not redistributed.

90-Second Research Project Path

Recommended project sequence:

Step	Question	Primary artifacts	What should be true
1	What is this project?	Project brief Project status Dashboard	A public-sample Xperience-10M research project with 20 tasks, baselines, and a scale-up plan.
2	What data is used?	Dataset-card alignment Official HF dataset Sample HF dataset	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?	window table feature manifest available-modality data	Each window is an aligned multimodal unit with video, audio, depth, pose/SLAM, mocap, IMU, calibration, and language-derived signals.
4	What are the 20 tasks?	20-task suite note task contract data task walkthroughs walkthrough data	Every task has a human-readable name, input, output, metric, baseline scores, and an explicit artifact path.
5	How are tasks evaluated?	evaluation protocol note evaluation-protocol data	The window unit, chronological split, leakage controls, task metrics, and current limitations are explicit.
6	What do current results mean?	research takeaways takeaway data summary metrics	Current metrics describe sample-level task behavior and identify which signals need larger held-out experiments.
7	Which models are implemented?	summary report neural MLP outputs HF baseline repo	Each task has minimal and neural-head evidence over the same feature windows.
8	What research directions does this support?	research roadmap direction data extension-probe data task contract data	The unified tasks are mapped to human modeling, 3D/4D reconstruction, egocentric interaction, and world modeling.
9	Which foundation model comes next?	foundation-model plan foundation-model data Native pretraining plan	Qwen3-Omni is the first held-out LoRA baseline; Cosmos 3 has Nano compatibility and Super forward-dynamics LoRA; policy models wait for robot-compatible action targets.
10	How can the 128-episode suite be pushed without more data?	selected-128 enhancement note selected-128 enhancement data	The enhancement pack proposes dense windows, hierarchical action/subtask labels, raw-feature shard priorities, and `multiscale_20s10_40s20_80s40` as the next export target.
11	How do I reproduce it?	reproducibility guide reproduction audit	Public commands and expected outputs are documented for the sample-episode task suite.
12	What is still pending?	verified Omni result data data access status multi-episode access status	The final held-out diagnostic Qwen pass is verified and JSON-validity target is met; strong action/subtask model quality remains pending.

A compact project-path summary is available at project-packet data.

Supporting Files

The artifact guide maps the public evidence 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.

The artifact index is the compact machine-readable companion used by the website and Hugging Face artifact dataset.

The project resource map is the complete generated inventory for scripts, result artifacts, website data, visual assets, configs, notes, and prepared Hugging Face bundles. Its structured companion, docs/data/project_resource_map.json, is used by the website Resources tab to search, filter, and open scripts/results directly.

Evaluation Protocol

The evaluation protocol and evaluation-protocol data 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 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 repo provides the sample episode used for the implemented task suite here.

This project keeps two evidence lines separate. Line 1 uses the public sample for raw-file inspection, task construction, and local reproducibility. Line 2 uses selected 128-episode public-safe artifacts for same-split method comparison, Qwen3-Omni v6 diagnostics, and Cosmos3 diagnostics. 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 the dataset-card alignment note and dataset-alignment data for full upstream-card and access-term context. Line 1 is the task lab; Line 2 is the selected-128 comparison layer; compact-proxy cells stay explicitly marked where direct raw targets are missing.

Start with the visual dashboard:

chaoyue0307.github.io/ropedia-xperience-10m-task-suite

Hugging Face Space app:

cy0307-ropedia-xperience-10m-task-suite.hf.space

Evidence Views

View	What to inspect	Why it matters
Project status	project status project-status data	Summarizes the current project state in one table.
Data contract	window table feature manifest modality manifests	Confirms what each sample window contains before modeling.
Dataset context	dataset-card alignment official dataset links	Explains the official dataset, public sample, modalities, access boundary, and what this repo uses.
Visual assets	figure index site assets	Shows the task-suite graphic, modality thumbnails, pipeline diagrams, charts, and logo assets.
Evaluation protocol	evaluation protocol note evaluation-protocol data	Defines the task unit, split, metrics, leakage controls, and current limitations.
Research roadmap	research roadmap roadmap data	Shows the path from sample-level task development to multi-episode work, larger model tracks, and the future native-pretraining goal.
Additional development directions	additional development directions direction data	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	native pretraining plan	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	Groups the public evidence after the project overview.
Reproducibility contract	reproducibility guide reproducibility matrix	States public commands, expected outputs, exact-match reproduction evidence, and non-reproducible boundaries.
Citation metadata	citation metadata software metadata 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
This project website	chaoyue0307.github.io/ropedia-xperience-10m-task-suite
This Hugging Face Space	huggingface.co/spaces/cy0307/ropedia-xperience-10m-task-suite
Live Hugging Face app	cy0307-ropedia-xperience-10m-task-suite.hf.space
GitHub Container package	ghcr.io/chaoyue0307/ropedia-xperience-10m-task-suite
Derived artifacts on Hugging Face	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
Consolidated weights, results, and analysis package	huggingface.co/cy0307/ropedia-xperience-10m-weights-results
Qwen3-Omni 128-episode LoRA adapter	huggingface.co/cy0307/ropedia-qwen3-omni-lora-128ep
Cosmos3-Super forward-dynamics LoRA adapter	huggingface.co/cy0307/ropedia-cosmos3-super-forward-dynamics-lora-128ep
Hugging Face collection	huggingface.co/collections/cy0307/ropedia-xperience-10m-task-suite
Xperience-10M dataset website	ropedia.com/dataset
Xperience-10M release page	ropedia.com/blog/20260316_xperience_10m
Ropedia GitHub organization	github.com/Ropedia
HOMIE Toolkit	github.com/Ropedia/HOMIE-toolkit
Xperience-10M Hugging Face dataset	huggingface.co/datasets/ropedia-ai/xperience-10m
Xperience-10M sample on Hugging Face	huggingface.co/datasets/ropedia-ai/xperience-10m-sample
Ropedia Hugging Face organization	huggingface.co/ropedia-ai

Citation

If you use this project, cite the Ropedia Xperience-10M Task Suite by Chaoyue He and also cite the upstream Ropedia Xperience-10M dataset according to its official dataset card. The repository includes CITATION.cff, codemeta.json, and docs/data/project_manifest.json for machine-readable metadata.

Copy this BibTeX entry for the repository:

bibtex

@misc{he2026ropedia_xperience10m_task_suite,
  author       = {Chaoyue He},
  title        = {Ropedia Xperience-10M Task Suite},
  year         = {2026},
  url          = {https://github.com/ChaoYue0307/ropedia-xperience-10m-task-suite},
  note         = {Public task and evaluation layer for Ropedia Xperience-10M: two evidence lines, 20 embodied-AI tasks, 180 scored method-task records, and selected-128 model diagnostics}
}

License And 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 and DATA_NOTICE.md.

Ropedia Xperience-10M task-suite infographic

The infographic uses a custom text-free research background and puts the shared processing contract plus all 20 unified task families in one figure. Public sample stream thumbnails remain available through the raw sample browser and derived modality assets instead of a separate repeated atlas panel. The task names, input/output summaries, and metrics are overlaid from results/episode_task_suite/summary_report.json with 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 complete unified task list is documented in TASK_SUITE_20.md and docs/data/task_suite_20.json. Historical tier2_task_suite paths remain only as stable provenance links inside the same suite.

The unified radar is now a grouped small-multiple comparison board instead of a nine-method overlay. It keeps all 20 task axes and all 9 method rows visible, but separates the methods into single-episode, 128-episode metadata/text, 128-episode raw-feature, and foundation-model panels. Every method has 20 explicit result records in the public matrix. Tasks 15 and 19 are marked as compact-proxy completions where the 128 export lacks raw interaction strings or paired video-view embeddings; those six proxy cells stay explicitly marked instead of being blended into direct-target metrics. The SVG uses sqrt(normalized_score) only for visual radius so small but real differences are readable; raw metrics and exact linear normalized scores remain in JSON and the table. Cosmos3-Super forward-dynamics LoRA remains a separate artifact card because its camera-pose proxy MSE is not one of the 20 task metrics. The machine-readable copies are docs/data/unified_task_model_radar.json and docs/data/task_method_20_result_matrix.json; the explicit score/proxy ledger is docs/data/task_method_20_gap_audit.json and TASK_METHOD_20_GAP_AUDIT.md; the public matrix is TASK_METHOD_20_RESULT_MATRIX.md. The website Results section also renders the same 180 cells as a wide, source-linked table with raw values, normalized radar values, metric keys, and direct/proxy badges.

The same source data is also split into two focused radars:

The single-episode radar uses one enlarged panel for Minimal vs Neural MLP, both with 20/20 scored public-sample axes. The 128-episode radar uses three grouped panels for metadata/text baselines, raw-feature baselines, and foundation-model rows: metadata and raw-feature simple/NN baselines are now complete 20/20 multi-episode records, and Qwen3-Omni v6 LoRA, Cosmos3-Super Reasoner, and Cosmos3-Nano Future Window each carry 20 scored task records. The current matrix has 180/180 scored method-task records.

The website raw sample browser includes a concise stream-to-feature ledger backed by docs/data/modality_atlas.json and docs/assets/modalities/. Those assets are small derived thumbnails from the public sample, not raw Xperience-10M files.

Minimal and neural task model architectures

The pipeline and architecture figures use the same pattern: text-free visual backgrounds carry the composition, while 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 with two public evidence lines. Line 1 is built from one public sample episode. Line 2 uses a selected 96/16/16 split over 128 episode paths, public-safe processed features, and verified Qwen3-Omni/Cosmos3 diagnostic artifacts.

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             # public-sample task definitions
  neural_task_models.py             # optional PyTorch MLP heads for task contracts
  research_direction_taxonomy.py    # maps walkthrough-backed tasks to the four research tracks
  research_direction_extension_tasks.py # one extra data-backed probe per track
  tier2_task_suite.py              # historical-name provenance builder for unified task rows
  build_unified_task_suite.py       # builds TASK_SUITE_20.md and task_suite_20.json
  build_unified_task_model_radar.py # builds grouped 20-axis model comparison radars
  build_task_method_20_gap_audit.py # builds the explicit 180/180 scored-cell ledger
  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
    score_existing_model_output_task_probes.py # scores task targets already present in verified model outputs
    collect_qwen3_v4_release_artifacts.py # pulls verified v4 results after remote eval

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/               # 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
    tier2_task_suite/               # provenance baseline tasks + predictions; historical path
    task_walkthroughs/              # case-study walkthroughs for walkthrough-backed tasks
  omni_exploration/                 # ModelScope readiness-check artifacts
  omni_finetune/model_output_task_probes_20260616/ # task-13/task-16 probes derived from verified model JSON

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/task_suite_20.json           # unified 20-task suite bundle
  data/unified_task_model_radar.json # 20-task radar values, groups, and sources
  data/single_episode_task_model_radar.json # 1-episode grouped radar values
  data/episode128_task_model_radar.json # 128-episode grouped radar values
  data/task_method_20_result_matrix.json # 9-method x 20-task result matrix
  data/task_method_20_gap_audit.json # explicit 180/180 scored-cell ledger
  data/task_icon_manifest.json     # assigned icon asset map for all 20 tasks
  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_suite_enhancement_128.json # no-new-episode 128-suite enhancement pack
  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
  assets/task-icons/*.svg           # one crisp assigned icon per task
  assets/task-icons/task-icon-atlas.png # generated overview atlas for the 20-task visual language
  data/tier2_task_suite.json       # provenance baseline bundle; historical path
  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 # task-suite 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 task-head architecture map
  assets/charts/unified_task_model_radar.svg # 9-method grouped small-multiple radar board
  assets/charts/single_episode_task_model_radar.svg # 1-episode enlarged radar panel
  assets/charts/episode128_task_model_radar.svg # 128-episode grouped radar panels
  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 project 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

<workspace>/
  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 website also includes a Raw Sample Browser that lists every official sample file, plays compact browser-preview clips derived from the official MP4 streams, exposes the audio track embedded in fisheye_cam0.mp4, links the full raw Hugging Face source for each MP4/HDF5/RRD file, and describes the annotation.hdf5 group organization without copying large raw files into this repository. The same manifest also lists what the non-playable annotation.hdf5 and visualization.rrd files contain, how each relates to the synchronized MP4 streams and 20-frame windows, and which external tools can open them.

The public sample dataset identifier is:

ropedia-ai/xperience-10m-sample

Hugging Face URL:

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 public-sample task definitions with lightweight neural heads:

bash

pip install torch
python scripts/episode_task_suite.py \
  --workspace /path/to/workspace \
  --include-neural

Then rebuild the unified 20-task index after the historical provenance bundle is regenerated:

bash

python scripts/tier2_task_suite.py --workspace /path/to/workspace
python scripts/build_unified_task_suite.py
python scripts/build_evaluation_protocol.py

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 selected-episode held-out package is now verified as a diagnostic result, not a strong final action/subtask 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.

Xperience-10M to Qwen3-Omni LoRA training flow

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. The current cross-version comparison is generated at docs/data/omni_model_comparison.json and results/omni_finetune/OMNI_MODEL_COMPARISON.md; it separates the single-episode task suite, 128-episode aligned simple/NN baselines, Qwen3-Omni v6 LoRA, Cosmos3-Super Reasoner, and Cosmos3-Nano Future Window packages. The same generated files also include model_groups: a model-first view that pairs 1-episode and 128-episode entries for the same family. Use that section when comparing task heads against task heads, Qwen3-Omni smoke/LoRA against Qwen3-Omni LoRA, or Cosmos3-Nano compatibility against future Cosmos weight releases. For Qwen3-Omni specifically, read QWEN3_OMNI_RUN_LINEAGE.md: v1-v4 are pipeline-hardening and ablation evidence, v5 is the pinned prior multiscale release, and v6 is the current public 20-task Qwen row.

The no-new-episode enhancement plan is recorded in docs/data/task_suite_enhancement_128.json and TASK_SUITE_ENHANCEMENT_128.md. It keeps the current Qwen3-Omni v6 and Cosmos3 packages as baselines, then defines dense-window scenarios, hierarchical action/subtask targets, task bottlenecks, and experiment cards for stronger selected-128 runs without overwriting earlier results.

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
selected_source_feature_index: XPERIENCE10M_128_EPISODE_FEATURE_INDEX.md and docs/data/xperience10m_128_episode_feature_index.json
processed_128_feature_artifacts: 34,269 Qwen3-Omni v6 multiscale windows, 106,095 dense multiscale compact rows, and 34,269 x 394 metadata/text matrix rows, all linked back to official gated ropedia-ai/xperience-10m episode paths
verified_final_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
final_train_loss / final_val_loss: 0.0277 / 0.0278
current_quality_target: strict-label JSON validity 100.00%, meeting the 98% target; action/subtask quality remains weak
qwen3_lora_adapter_repo: https://huggingface.co/cy0307/ropedia-qwen3-omni-lora-128ep
cosmos3_super_lora_adapter_repo: https://huggingface.co/cy0307/ropedia-cosmos3-super-forward-dynamics-lora-128ep
128_aligned_baselines: unified 20-task axes for simple and neural baselines, including metadata/text rows and public-safe compact-proxy rows where raw-feature targets are required
cosmos3_nano: verified Cosmos3-Nano future-window compatibility package, 378 held-out future-window predictions from 14 test episodes
cosmos3_super_reasoner: verified Cosmos3-Super Reasoner base-weight JSON-task evaluation, 448 held-out predictions from 14 test episodes; JSON validity 51.12%, action macro-F1 0.0008, contact accuracy 32.14%, transition accuracy 36.83%
cosmos3_super_forward_dynamics_lora: verified 8-GPU FSDP LoRA artifact over camera-pose proxy targets; 2,848 train rows, 512 val rows, 448 test rows, 26.2M adapter parameters, val MSE 4.0082, test MSE 3.6853; public package excludes safetensors
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 latest verified diagnostic package uses the same selected split and 8-GPU training path, includes the full held-out evaluation with 4,032 predictions and 99.90% JSON validity, and keeps raw data plus full Qwen weights out of the public repos. The next pass should keep this package contract while improving action/subtask target quality and 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 <train_run_id> \
  --eval-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 <train_run_id> \
  --eval-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 tracks 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

CUDA_DEVICE_GROUPS="0,1 2,3 4,5 6,7" \
SHARDS=4 \
RUN_ID=<merged_eval_run_id> \
scripts/omni/run_qwen3_omni_lora_eval_sharded.sh

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 tracks 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 final selected-episode diagnostic run.

Keep weight-bearing repositories model-specific: the final 128-episode Qwen3-Omni adapter belongs in cy0307/ropedia-qwen3-omni-lora-128ep, older Qwen smoke material remains historical. Cosmos3-Nano remains an artifacts-only compatibility result; Cosmos3-Super Forward-Dynamics now has a separate weight-bearing model repo at cy0307/ropedia-cosmos3-super-forward-dynamics-lora-128ep. Metrics, predictions, audits, and reports stay in the artifact dataset.

bash

python3 scripts/omni/upload_qwen3_omni_lora_to_hf.py \
  --repo-id cy0307/ropedia-qwen3-omni-lora-128ep \
  --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 tracks 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 track	Use now for future-window compatibility analysis and the verified Cosmos3-Super forward-dynamics LoRA artifact; compare its loss metrics separately from Qwen JSON-task accuracy.
GR00T	Humanoid/action-policy track	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 and 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 for the long-term full-corpus pretraining plan.

The three headline foundation directions are also separated as pipeline tracks so each track is easy to read without mixing current results and future work:

Pipeline track	First concrete pipeline	Current scope
Spatial intelligence models	Build scene/object memory targets from multiview RGB, depth, pose, calibration, object cues, and language prompts.	Ready as a geometry/reasoning pipeline; the next readout is held-out spatial QA, pose consistency, counting, and scene-memory metrics.
Human-video world models	Predict next action, next subtask, future object set, contact transition, and future state from observed interaction windows.	Partially evidenced by future-task probes and Cosmos-style artifacts; visual/latent future quality still needs stronger metrics.
Vision-language-action models	Convert egocentric video, captions, hand/body motion, contacts, and objects into action chunks or policy-compatible targets.	Feasible, but gated by action-token conversion, normalization, retargeting evidence, and held-out policy metrics.

For the single public sample, each direction is now shown as an explicit training-pair recipe:

Direction	One-sample input	One-sample output target
Spatial intelligence	20-frame windows from `windows.csv` / `shared_windows.npz`, joined with six MP4 camera streams plus `annotation.hdf5` depth, pose, SLAM/calibration, object/contact cues, and optional language questions.	Camera-view match, object relevance, object-set memory, depth/pose reconstruction proxy, caption-grounded retrieval, and spatial QA targets.
Human-video world model	Current observed window at time `t`: RGB/audio/sensor summaries, hand/body motion, camera pose, current object/contact state, and current action/subtask context only.	Shifted future targets: next action, next subtask, future object set, contact transition, time-to-transition, camera-motion delta, or latent/future feature.
Vision-language-action	Egocentric/fisheye video, caption/object context, hand/body mocap, contact state, and current subtask text as observation-language input.	Action-token proxies: current/next action, object-conditioned action relation, contact state, interaction-text class, subtask transition, or hand-trajectory/action-chunk proxy.

High-resolution slide diagrams for the three tracks are published in docs/assets/foundation-pipelines. Spatial intelligence and human-video world modeling use the clean slide PNGs supplied for publication and are exported as 2560-pixel public images. The 2026-06-19 refresh verified that the latest uploaded Spatial and Human-video PNGs are byte-identical to the committed clean source cache. The VLA card now uses the clean VLA slide PNG supplied afterward and is exported through the same 2560-pixel public path. These images are communication assets, not completed model-quality evidence; the exact task, training, and evaluation contracts remain in the Markdown and JSON files.

Spatial intelligence models

Human-video world models

Vision-language-action models

See THREE_FOUNDATION_PIPELINES.md and docs/data/three_foundation_pipelines.json.

Backbone-specific contracts now live in configs/omni_backbones. The extension contract is documented in OMNI_MODEL_EXTENSION_CONTRACT.md, and the registry can be checked with:

python scripts/omni/backbone_registry.py --validate --json

Verify that every configured backbone can pass the public-safe packaging contract on synthetic derived artifacts:

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/<eval_run_id>

Create a new planned backbone track 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 tracks 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 and docs/data/additional_development_directions.json.

Four Research Directions

The walkthrough-backed task contracts are organized against the four Ropedia research directions in a source-linked companion set:

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	Prerequisite evidence	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	World-model prerequisite evidence	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 task evidence. Directions A, B, and D need additional targets and multi-episode training before they become full research deliverables.

Four Direction Probes

Alongside the unified 20-task suite, the repo includes one 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.

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:

python scripts/research_direction_extension_tasks.py

These four probes make the direction mapping concrete while staying within the same single-episode evidence line. They should be read as task-backed probes, not as claims that the larger foundation-model directions are solved.

Unified 20-Task Suite

The sample task surface is presented as 20 tasks in one suite. All task rows share the same 20-frame window unit, 5-frame stride, chronological split, and minimal/neural comparison style, with task-specific leakage rules when a target would otherwise leak through caption, object, contact, or future features.

The historical tier2_task_suite file and directory names remain only for stable artifact links. They should be read as provenance bundles inside the unified 20-task suite, not as a separate benchmark tier.

The all-task table, including every input/output contract and minimal/neural metric, is in TASK_SUITE_20.md. Historical provenance links remain listed above for exact source tracing, but the public task surface should be read as one integrated 20-task suite.

Run:

/path/to/python-with-h5py scripts/tier2_task_suite.py

Regeneration needs either HOMIE-toolkit or an environment with h5py because the interaction/object targets come from the raw public-sample annotation.hdf5. The raw HDF5 and MP4 files remain excluded from the public repo and Hugging Face mirrors.

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:

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

Core Architecture Families in the 20-Task Suite

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/, and the rollup is stored in the neural_tasks section of results/episode_task_suite/summary_report.json.

The walkthrough-backed task 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 walkthrough-backed task contracts
Alternate audio representation	+0.0936 mean delta	n/a	Alternate audio-window representation improves over the baseline audio variant on 6 walkthrough-backed 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 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 original contracts
Mean current-audio delta	+0.0418
Tasks where alternate audio representation improves over baseline audio	6 / 12 original contracts
Mean alternate-representation delta vs baseline audio	+0.0936

Full files:

Neural MLP Results

The neural baseline was run locally with --include-neural for the original core task contracts 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, task metrics, feature manifest, and available modality manifest matched exactly after float normalization.

See 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.

Data Notice

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.

cy0307/ropedia-xperience-10m-task-baselines

Ropedia Xperience-10M Task Suite

Cite This Repository

Contents

Project Entry Points

At A Glance

Public Structure: 20 Tasks / 4 Directions / 3 Pipelines / 1 Unified Target

Data Explorer Analysis

Two Evidence Lines

Result Ledger

Method Blocks

Qwen3-Omni Run Versions

Fast Project Map

Why This Project Exists

Start Here

Glossary

Published Mirror Map

Research Project Overview

Current Research Scope

Project Status

90-Second Research Project Path

Supporting Files

Evaluation Protocol

Dataset Context

Evidence Views

Links

Citation

License And Metadata

Scope

What Is Inside

GitHub Package

Data Expected

Quickstart

Xperience-10M Fine-Tuning Exploration

Sample Count Decision

Multi-Episode Readiness Gate

Progressive Train/Validation Pilot

Full 128-Episode Held-Out Pilot

Uploading Qwen3-Omni LoRA artifacts

Foundation Backbone Plan

Additional Development Directions

Four Research Directions

Four Direction Probes

Unified 20-Task Suite

Task Walkthroughs For Juniors

Core Architecture Families in the 20-Task Suite

Key Results

Audio Contribution Study

Neural MLP Results

Single-Episode Diagnostics and Explorer

Reproducibility Check

Why Some Scores Are Low

Modalities Used

Data Notice

cy0307/
ropedia-xperience-10m-task-baselines