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+
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+ Pipeline from raw episode to task suite.
+Every script works from the same data contract: aligned multimodal windows, explicit labels, cached feature extraction, and artifact-first evaluation.
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+ 
+
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+
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+ 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.
+
+ 
+
+
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+ Baselines with transparent artifacts.
+Motion-only and all-modality classifiers use the same lightweight softmax model so the comparison stays readable on a laptop.
+
+
+ All-modality action
0.9791Motion-only subtask
0.9528All-modality subtask
0.9308
+
+ 
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+
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+ The 12 tasks use three minimal head families.
+The diagram separates the shared episode-window feature pipeline from the task-specific heads: linear softmax, ridge projection/regression, and multi-label logistic regression.
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+ 
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+ Single-episode tasks, deliberately scoped.
+These tasks turn one episode into a lab for temporal structure, modality alignment, retrieval, reconstruction, and action understanding.
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+ timeline_actionsupervised
All modalities to current action label. Chronological split exposes unseen future actions.
macro-F10.0500
timeline_subtasksupervised
All 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 modalities.
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
+ 
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+
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+ All-modality features are inspectable.
+The point is not hidden complexity. Every block has a clear source modality and an explicit dimensional footprint.
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+ Diagnostics separate easy wins from real gaps.
+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.
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+ 
+ 
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+ Everything important is checked in.
+Metrics, predictions, confusion matrices, feature manifests, model weights, and derived window artifacts are committed so the repo is reviewable without rerunning first.
+
+
+ Task-suite report
One JSON file with every task metric and split detail.
summary_report.jsonFeature manifest
Start/end index and dimension for every all-modality feature block.
feature_manifest.jsonCross-modal retrieval
The strongest self-supervised signal from the single episode.
metrics.jsonAll-modality action model
Classifier metrics, predictions, confusion matrix, and model weights.
metrics.jsonWindows table
Window start/end frames and aligned action/subtask labels.
windows.csvReproduction scripts
Three training scripts plus the dashboard generator.
scripts/
+
+
+
+ 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