BWM: A Low-Cost High-Fidelity World Simulator for Robot Learning
- Published
- Source
- arXiv
- Paper number
- 791
- Field
- Robotics
- arXiv ID
- 2607.29302
Key points
- An action-conditioned world model predicts future scenes conditioned on robot actions, combining initial environment guidance, dynamic history, and precise action control.
- We built a training-data pipeline that preserves action-observation alignment through trajectory replay, duplicate clip sampling, and enhanced initial observations.
- It ranks first overall among open-source entries in WorldArena Challenge Track 1 plus two Track 2 applications.
- The policy trained on BWM-generated data reaches a 71.00% average success rate on a real robot, a large improvement over the previous best world simulator at 53.33%.
- We release model checkpoints, training and inference code, and data-generation and policy-evaluation interfaces as open source.
Paper links
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