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Event-SAE Head full10 completed experiment
A public release of 3600 completed rollouts from a LIBERO-Spatial interpretability experiment using sparse autoencoders (SAE) with OpenVLA, including 18 conditions, 200 paired evaluation cases, and 16 ablated features for analyzing model behavior through feature interventions.
Downloads37
Episodes3600
Hours40.877
Why This Matters for Physical AI
This dataset enables research into neural network interpretability for robotic control by providing systematic ablation studies of learned features, helping understand which model components are critical for task success in embodied AI systems.
Technical Profile
- Environment
- simulation
- Episodes
- 3600
- Total Hours
- 40.877
- Data Format
- tar.gz
- Annotation Types
- language_instructionsreward_labelsaction_labels
Access
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