podolinsky
π0.5 LIBERO-10 — prefix features + K=10 action samples (normal + occluded)
Aligned rollouts of the π0.5 policy on LIBERO-10 tasks with K=10 action samples per inference in normal and occluded scene variants, paired with last-layer prefix features from the PaliGemma backbone for representation probing and failure detection.
Downloads18
Episodes500
Why This Matters for Physical AI
This dataset enables research into failure detection and robustness of generative policies through multi-sample action generation and prefix feature extraction, critical for understanding and improving vision-language-action model reliability in manipulation tasks.
Technical Profile
- Modalities
- videotabularrgblanguage
- Action Space
- action_chunks
- Environment
- simulation
- Task Types
- manipulationpick_and_place
- Episodes
- 500
- Data Format
- npz
- Annotation Types
- language_instructionsreward_labels
Access
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