DAVIAN-Robotics2026apache-2.0
RoboLab-BananaInBowl-hard-base-stoch-260802
RL rollouts from a RoboLab (Isaac Lab) BananaInBowl policy under the hard initial-pose distribution, collected via stochastic sampling from a trained RFCL agent for pi0.5 delta-action supervised fine-tuning.
Downloads22
Episodes5941
Why This Matters for Physical AI
Provides large-scale RL-generated manipulation trajectory data with standardized recording conventions and quantile statistics to support reproducible policy learning and imitation learning research.
Technical Profile
- Modalities
- rgbproprioception
- Robot Embodiments
- DROID
- Action Space
- joint_positions
- Environment
- simulation
- Task Types
- manipulationpick_and_place
- Episodes
- 5941
- Data Format
- LeRobot
- Annotation Types
- reward_labels
- License
- apache-2.0
Access
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