Qwen-RobotManip Technical Report: Alignment Unlocks Scale for Robotic Manipulation Foundation Models
- Published
- Source
- arXiv
- Paper number
- 439
- Field
- Robotics
- arXiv ID
- 2606.17846
Key points
- It proposes a three-dimensional alignment framework for representation, motion, and action, using canonical state, camera-frame delta pose, and in-context adaptation.
- It builds a human-to-robot synthesis pipeline that converts human egocentric video into trajectories for 15 robot platforms.
- It builds a pretraining corpus of about 38,100 hours of manipulation data using only open-source data, with no proprietary data collection.
- In out-of-distribution evaluation on LIBERO-Plus, RoboCasa365, and EBench, it outperforms π0.5 and GR00T-N1.7 across all axes.
- It ranks first on the RoboChallenge Table30-v1 generalist track and achieves a 20 percent relative improvement.
- It has been validated on four real robot platforms: AgileX ALOHA, Franka, UR, and ARX.
Paper links
External research summaries. These are not HDATF publications or measured product results.