One Policy, Many Embodiments: Unified Camera-Centric Action Geometry Pre-training for Heterogeneous Embodied Manipulation
- Published
- Source
- arXiv
- Paper number
- 1022
- Field
- Robotics
- arXiv ID
- 2608.26058
Key points
- It aligned robot, simulation, and human-hand data in a single camera-centric action space for unified pretraining.
- It directly incorporated 2,340 hours of human demonstrations into training as a 'human embodiment,' without retargeting or video synthesis.
- A single checkpoint achieved 98.3% on LIBERO, 88.7%/89.2% on RoboTwin Easy/Hard, and 82.0% zero-shot on LIBERO-Plus.
- On a real robot, it achieved 60% for picking up bread, 90% for opening a drawer, and 75% for stacking bowls, outperforming pi0.5 (20/85/65%) under the same conditions.
- It also disclosed limitations: camera-calibration and depth-estimation errors propagate directly, and cross-embodiment transfer remains difficult (35% from ALOHA to ARX).
Paper links
External research summaries. These are not HDATF publications or measured product results.