MarcelTornemit
put_cube_in_bowl_preferences
Real-robot trajectories for a cube-in-bowl manipulation task on a Franka Panda with human pairwise preference labels across multiple judgment axes. Built for reward-model and preference-learning research with 540 episodes and 2,348 preference labels.
Downloads0
Episodes540
Hours1.2
Why This Matters for Physical AI
This dataset enables training reward models and preference-based learning algorithms through rich pairwise preference annotations on real robot manipulation, advancing methods for learning from human feedback in robotics.
Technical Profile
- Modalities
- rgbproprioception
- Robot Embodiments
- Franka Panda
- Action Space
- joint_positions
- Environment
- lab
- Task Types
- manipulationpick_and_place
- Episodes
- 540
- Total Hours
- 1.2
- Data Format
- LeRobot
- Annotation Types
- language_instructionsreward_labelspairwise_preferences
- License
- mit
Access
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