Translation as a Bridging Action: Transferring Manipulation Skills from Humans to Robots
We study whether we can learn novel manipulation skills from human actions to a bi-manual robot with parallel grippers. Human action data is cheap, abundant, and diverse, making it one of the most promising resources for scaling up robot learning. Yet transferring skills from humans to robots remains hard: most prior work treats humans as just another bi-manual 6DoF embodiment, where hand-pose estimates are noisy and the contact patterns of human fingers differ fundamentally from those of a parallel gripper. We argue that learning rotation-inclusive action signals from human data is therefore sub-optimal, and instead propose a bridging action representation: the relative wrist translation...
| Authors | Sijin Chen, Kaixuan Jiang, Haixin Shi et al. |
|---|---|
| Primary category | cs.RO |
| Published | 26 June 2026 |
| Last updated at source | 26 June 2026 |
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