SkiP: When to Skip and When to Refine for Efficient Robot Manipulation
Previous imitation learning policies predict future actions at every control step, whether in smooth motion phases or precise, contact-rich operation phases. This uniform treatment is wasteful: most steps in a manipulation trajectory traverse free space and carry little task-relevant information, while a small fraction of \emph{key} steps around contacts, grasps, and alignment demand dense, high-resolution prediction. We propose a novel \emph{action relabeling} mechanism: at each timestep in a skip segment, we replace the behavior cloning target with the action at the entrance of the next key segment, enabling the policy to leap over redundant steps in a single decision. The resulting \te...
| Authors | Mingtong Dai, Guanqi Peng, Yongjie Bai et al. |
|---|---|
| Primary category | cs.RO |
| Published | 15 May 2026 |
| Last updated at source | 15 May 2026 |
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