Cross-Embodiment Transfer via Behavior-Aligned Representations
Recent progress in large-scale imitation learning for robot manipulation has been driven by leveraging datasets across a wide range of robot embodiments. However, achieving significant cross-embodiment transfer is often still challenging. In this work, we study the role of using behavior-aligned representations (e.g., object bounding boxes, language motions, end-effector traces of robot motion) in vision-language-action (VLA) models to promote cross-embodiment transfer. We hypothesize that by possessing invariances across embodiments while being predictive of robot actions, these representations can help unify large-scale cross-embodiment data to enhance transfer. To assess our hypothesis...
| Authors | Ajay Sridhar, Jensen Gao, Jonathan Yang et al. |
|---|---|
| Primary category | cs.RO |
| Published | 30 July 2026 |
| Last updated at source | 30 July 2026 |
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