podolinsky
GR00T-N1.7 LIBERO-10 backbone features + K=10 action samples (normal + occluded)
Aligned rollouts of the NVIDIA GR00T-N1.7-LIBERO policy on LIBERO tasks in normal and occluded variants, with K=10 action samples per inference and per-token backbone features from the Cosmos-Reason2-2B VLM backbone. Designed for representation probing and sampling-based failure detection research.
Downloads8
Episodes500
Why This Matters for Physical AI
This dataset enables research into failure detection and robustness of generative policies by providing multiple action samples and backbone features that allow analysis of model uncertainty and occlusion-induced failures in manipulation tasks.
Technical Profile
- Modalities
- rgbproprioceptionlanguage
- Robot Embodiments
- Franka Panda
- Action Space
- end_effector_delta
- Environment
- simulationkitchenliving_roomstudy
- Task Types
- manipulationpick_and_placeobject_placement
- Episodes
- 500
- Data Format
- npz
- Annotation Types
- language_instructionsreward_labelsaction_labels
Access
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