Datasets:
Ropedia Episode Task Suite
An audit-first embodied-AI learning repo built around one public Ropedia / Xperience-10M sample episode.
The project does one narrow thing carefully: it turns a raw multimodal episode into:
- manifested sliding-window features over the currently extracted modalities,
- motion-only and current all-feature baseline models,
- 12 end-to-end episode-level tasks,
- metrics, predictions, model weights, manifests, charts, and a static website,
- a clear explanation of what a single episode can and cannot prove.
Dataset Modality Coverage
The Xperience-10M sample is a 4D multimodal episode source spanning video, audio, depth, pose, motion capture, inertial sensing, and language annotation. This repo keeps that distinction explicit:
- the raw sample files include six MP4 video streams with AAC audio streams,
annotation.hdf5includes depth, SLAM/camera pose, hand/body mocap, IMU, and language annotation,- the current minimal 8,378-d baseline feature manifest includes video, depth, pose/SLAM, mocap, IMU, calibration, and language blocks,
- audio is documented in the figures but is not yet extracted as a model input feature block in this minimal baseline.
Start with the visual dashboard:
https://chaoyue0307.github.io/ropedia-episode-task-suite/
Hugging Face Space app:
https://cy0307-ropedia-episode-task-suite.static.hf.space/
Read This Project In Three Layers
| Layer | What to inspect | Why it matters |
|---|---|---|
| Data contract | windows.csv, feature_manifest.json, modality manifests |
Confirms what each sample window contains before modeling |
| Minimal heads | softmax, ridge projection/regression, multi-label logistic heads | Keeps every input/output contract visible and debuggable |
| Evidence | metrics, predictions, confusion matrices, diagrams, dashboard | Makes the single-episode claims reviewable without rerunning first |
Links
The infographic uses a ChatGPT-image-generated text-free research background and
low-resolution modality thumbnails extracted from the public sample episode. 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 pipeline and architecture figures use the same pattern: ChatGPT-image
provides text-free visual backgrounds, 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. It does not claim cross-episode generalization because the public sample used here is one episode. The correct next step for real model claims is to run the same suite over many episodes and split train/test by held-out episode.
What Is Inside
scripts/
train_min_action_model.py # motion/IMU baseline
train_all_modalities_model.py # current all-feature lightweight baseline
episode_task_suite.py # 12 end-to-end task definitions
generate_visualizations.py # refreshes SVG charts + summary JSON
render_task_suite_infographic.py # renders the ChatGPT-image-backed PNG
render_overview_figures.py # renders polished pipeline/architecture PNGs
results/
min_action_model/ # motion-only action baseline artifacts
min_subtask_model/ # motion-only subtask baseline artifacts
min_all_modalities_action_model/ # current all-feature action artifacts
min_all_modalities_subtask_model/ # current all-feature subtask artifacts
episode_task_suite/ # 12-task suite metrics and predictions
docs/
index.html # GitHub Pages dashboard
data/summary_metrics.json # website-readable metrics bundle
assets/task_suite_infographic.png # 12-task presentation graphic
assets/pipeline_diagram.png # verified episode pipeline graphic
assets/task_architectures.png # verified 12-task minimal architecture map
assets/charts/*.svg # regenerated visualizations
notes/
min_action_model.md
all_modalities_model.md
episode_task_suite.md
Raw Ropedia data is not committed. Download it from the original source and follow the dataset terms.
Data Expected
The scripts expect a workspace with the Ropedia toolkit and the sample episode:
<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 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:
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:
hf download ropedia-ai/xperience-10m-sample \
--repo-type dataset \
--local-dir data/sample/xperience-10m-sample
Clone and run this repo:
git clone https://github.com/ChaoYue0307/ropedia-episode-task-suite.git
cd ropedia-episode-task-suite
python scripts/episode_task_suite.py --workspace /path/to/workspace
Run the smaller baselines:
python scripts/train_min_action_model.py --workspace /path/to/workspace
python scripts/train_all_modalities_model.py --workspace /path/to/workspace
Refresh charts and the website data bundle:
python scripts/generate_visualizations.py
python scripts/render_overview_figures.py
python scripts/render_task_suite_infographic.py
Minimal 12-Task Architectures
These are deliberately minimal baselines. They are useful because every input/output contract is explicit, not because they are strong embodied-AI models.
Shared setup:
raw episode -> 20-frame windows, stride 5 -> 8,378-d current feature vector
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 | timeline_action, timeline_subtask, transition_detection, next_action, contact_prediction, temporal_order, misalignment_detection |
z-score features, then XW+b, softmax, cross-entropy, L2 |
| Dual ridge regression/projection | hand_trajectory_forecast, modality_reconstruction |
z-score input/target, solve ridge regression with L2=10 |
| Ridge + cosine ranking | caption_grounding, cross_modal_retrieval |
project one modality into another feature space, then rank candidates by cosine |
| Multi-label logistic regression | object_relevance |
z-score non-caption features, sigmoid object heads, threshold at 0.5 |
The task-specific heads are:
| Task | Input | Minimal head | Output |
|---|---|---|---|
timeline_action |
all featurized modalities | linear softmax | current action class |
timeline_subtask |
all featurized modalities | linear softmax | current subtask class |
transition_detection |
all featurized modalities | linear softmax | steady vs action boundary |
next_action |
all featurized modalities at t |
linear softmax | action at t+20 frames |
hand_trajectory_forecast |
all featurized modalities at t |
ridge regression | future 10-frame left/right hand joints |
contact_prediction |
non-contact and non-caption feature blocks | linear softmax | any body contact |
object_relevance |
non-caption feature blocks | multi-label logistic | relevant object set |
caption_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 |
modality_reconstruction |
motion/IMU/camera | ridge regression | depth/video feature vector |
temporal_order |
[x_t, x_t+1, x_t+1-x_t] |
binary linear softmax | correct vs reversed order |
misalignment_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 | 0.9791 macro-F1 | 0.9828 | 8,378-dimensional feature vector |
| Motion-only subtask | 0.9528 macro-F1 | 0.9759 | Strong within-episode subtask signal |
| Current all-feature subtask | 0.9308 macro-F1 | 0.9828 | High accuracy, lower class-balanced score |
| Cross-modal retrieval | 0.3764 top-5 | n/a | Motion/IMU/camera retrieves matching depth/video |
| Transition detection | 0.6552 macro-F1 | 0.9253 | Boundary F1 is 0.2143 |
| Hand trajectory forecast | 0.8223 MPJPE | n/a | Predicts future hand-joint trajectory |
The strongest single-episode self-supervised signal is cross-modal retrieval: motion/IMU/camera features retrieve matching depth/video windows substantially better than random.
Reproducibility Audit
I re-ran the full pipeline from the local raw public sample into
/private/tmp/ropedia-audit and compared regenerated metrics with the committed
artifacts. The baseline metrics, 12 task metrics, feature manifest, and
available modality manifest matched exactly after float normalization.
See notes/reproducibility_audit.md for the
commands and verification evidence.
Why Some Scores Are Low
The task suite intentionally uses a chronological split:
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.
Feature Blocks Used
The current feature vector has 8,378 dimensions and includes:
- hand/body mocap joints and contact labels,
- camera translation and rotation,
- IMU acceleration and gyroscope traces,
- depth confidence features,
- six video streams,
- caption/object/interaction text features,
- SLAM point-cloud summary features,
- calibration parameters.
It does not yet include an audio feature block.
The exact feature block boundaries are stored in
results/episode_task_suite/feature_manifest.json.
Data Notice
Ropedia / Xperience-10M data belongs to its original authors and is subject to the dataset's original license and access terms. This repo contains code and derived experiment artifacts only; it does not redistribute the raw videos or raw annotation dataset.