MarcelTorneMIT
fold_pants_preferences
Real-robot trajectories for folding shorts on a Franka Panda with human pairwise preference labels across 45 judgment axes. Built for reward modeling and preference learning research with 536 episodes and 4,390 preference labels.
Downloads0
Episodes536
Hours~9.8
Why This Matters for Physical AI
This dataset enables research in reward modeling and reinforcement learning from human feedback (RLHF) by providing fine-grained pairwise preference judgments across multiple task-specific axes, advancing methods for learning complex manipulation behaviors from human preferences.
Technical Profile
- Modalities
- rgbproprioception
- Robot Embodiments
- Franka Panda
- Action Space
- joint_positions
- Environment
- lab
- Task Types
- manipulationfolding
- Episodes
- 536
- Total Hours
- ~9.8
- Data Format
- LeRobot
- Annotation Types
- preference_labelspairwise_comparisonsreward_labelstask_success_flags
- License
- MIT
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