NeurIPsMay1234MIT
openpi-interpretability-data
Interpretability artifacts (activations, conceptors, linear steering vectors, sparse autoencoder vectors and checkpoints) extracted from open vision-language-action (VLA) policy models on the LIBERO, MetaWorld, and RoboCasa benchmarks.
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Why This Matters for Physical AI
This dataset provides interpretability artifacts from vision-language-action models trained on standard robotics benchmarks, enabling research into understanding and steering learned robotic behaviors through mechanistic interpretability techniques.
Technical Profile
- Modalities
- rgblanguage
- Action Space
- language
- Environment
- simulationlab
- Task Types
- manipulationpick_and_place
- Data Format
- PyTorch
- Annotation Types
- language_instructionsaction_labelsreward_labels
- License
- MIT
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