DC-WAM: Dynamic-Centric Visual Supervision and Reasoning for World-Action Models
- Published
- Source
- arXiv
- Paper number
- 756
- Field
- Robotics
- arXiv ID
- 2607.25918
Key points
- We redistribute visual supervision from appearance reconstruction to interaction-centered dynamics in RGB video prediction, so PSNR drops but policy performance improves.
- A temporal difference flow-matching objective and tracker-guided weighting focus on robot-object contact motion rather than static background and texture.
- The DynaRoute attention bias predicts dynamic-relevant tokens and steers visual attention toward them.
- On LIBERO-Plus OOD, it reaches 60.9%, a 9.4-point gain over FastWAM, and reduces the ID-OOD gap from 46.1 to 37.2.
- On real robot bimanual tasks, it achieves up to a 30-point improvement under lighting changes.
Paper links
External research summaries. These are not HDATF publications or measured product results.