JoyAI-RA 0.5: Scaling Robot Manipulation Learning via Dual Action Alignment
- Published
- Source
- arXiv
- Paper number
- 836
- Field
- Robotics
- arXiv ID
- 2608.05674
Key points
- The authors built a world model, LAC-WM, that learns physical dynamics by extracting latent actions from human videos.
- We propose an explicit alignment method that aligns human and robot trajectories in a unified physical action space.
- With inner-loop and outer-loop reinforcement learning, it achieves both fast adaptation and improvement of the base policy.
- As the scale of first-person human video increases, robot performance improves consistently and does not saturate.
- It shows strong performance on both seen tasks and new variants on a real AgiBot robot.
Paper links
External research summaries. These are not HDATF publications or measured product results.