Ropedia Episode Task Suite

Ropedia 12-task suite, first.

The top-level map shows the Xperience-10M sample modalities and the verified 12-task results. Audio is present in the sample MP4 stream, but the current 8,378-d baseline manifest does not featurize it.

ChatGPT-image-backed infographic showing all 12 Ropedia episode tasks

From raw episode to checked artifacts.

Every script works from one data contract: aligned multimodal windows, explicit labels, cached feature extraction, and a manifest that makes omitted modalities visible.

Verified Ropedia multimodal pipeline diagram

What this project proves

It proves the full engineering loop: reading Ropedia sample data, aligning modalities, converting them into model-ready windows, defining meaningful tasks, producing metrics, and packaging every artifact for review.

What it does not prove

It does not claim general embodied intelligence. A single episode cannot support cross-environment generalization; that requires many episodes and held-out episode splits.

Small baselines, no hidden machinery.

Motion-only and current all-feature classifiers use lightweight heads so the comparison stays readable on a laptop and easy to audit.

Motion-only action

0.9688macro-F1, 18 classes

Current all-feature action

0.9791macro-F1, 8,378 features

Motion-only subtask

0.9528macro-F1, 14 classes

Current all-feature subtask

0.9308macro-F1, chronological caveats

The 12 tasks share four head families.

The diagram separates the shared episode-window feature pipeline from the task-specific heads, and notes that audio remains dataset context rather than a current baseline feature block.

Verified minimal architecture diagram for all 12 Ropedia episode tasks

Task cards and metrics.

The same 12 tasks are kept filterable here so the supervised, forecast, retrieval, and diagnostic probes can be inspected individually.

timeline_actionsupervised

All featurized modalities to current action label. Chronological split exposes unseen future actions.

macro-F10.0500

timeline_subtasksupervised

All featurized modalities to current subtask label. Useful for segmentation diagnostics.

macro-F10.0495

transition_detectiondiagnostic

Predict steady vs action boundary. Highlights task-transition localization quality.

macro-F10.6552

next_actionsupervised

Current multimodal window to action 20 frames later. Tests short-horizon task flow.

macro-F10.0593

hand_trajectory_forecastforecast

Predict future left/right hand 3D joints. Closer to imitation-learning style signals.

MPJPE0.8223

contact_predictionsupervised

Non-contact modalities to binary contact. Degenerate in this sample because one class dominates.

accuracy1.0000

object_relevancesupervised

Predict relevant object set from non-caption feature blocks.

micro-F10.1839

caption_groundingretrieval

Caption objects/interaction query to matching sensor window.

MRR0.0172

cross_modal_retrievalretrieval

Motion/IMU/camera query retrieves matching depth/video window. Strongest single-episode signal.

top-50.3764

modality_reconstructionforecast

Motion/IMU/camera to depth/video feature vector.

R2-0.0160

temporal_orderdiagnostic

Two adjacent windows to correct vs reversed order.

F10.5487

misalignment_detectiondiagnostic

Motion+visual pair to aligned vs shifted by eight windows.

F10.4866

Every feature block has a source.

The point is not hidden complexity. Every block has a source modality, a dimensional footprint, and a manifest entry.

Diagnostics separate memorization from signal.

The charts make the main lesson visible: within-episode supervised labels are easy under some splits, while retrieval, grounding, forecasting, and alignment remain the useful probes.

Where the evidence lives.

Metrics, predictions, confusion matrices, manifests, model weights, and derived window artifacts are committed so the repo is reviewable before rerunning anything.

Task-suite report

One JSON file with every task metric and split detail.

summary_report.json

Feature manifest

Start/end index and dimension for every current feature block.

feature_manifest.json

Cross-modal retrieval

The strongest self-supervised signal from the single episode.

metrics.json

Current all-feature action model

Classifier metrics, predictions, confusion matrix, and model weights.

metrics.json

Windows table

Window start/end frames and aligned action/subtask labels.

windows.csv

Reproduction scripts

Three training scripts plus the dashboard generator.

scripts/

Hugging Face Space

The same dashboard packaged as a public static Space.

cy0307/ropedia-episode-task-suite

Derived HF artifacts

Metrics, predictions, docs, and lightweight derived files without raw Ropedia video/data redistribution.

dataset repo

HF baseline models

Minimal NumPy softmax and ridge baseline weights with model card and architecture diagrams.

model repo

HF collection

Space, artifacts, and model baselines grouped into one public project collection.

collection

Reproduce the suite.

Raw Ropedia data is not redistributed here. Download the public sample separately, then run the same scripts.

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

hf download ropedia-ai/xperience-10m-sample \
  --repo-type dataset \
  --local-dir data/sample/xperience-10m-sample

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