SkiP: When to Skip and When to Refine for Efficient Robot Manipulation
The paper introduces SkiP, a novel approach for efficient robot manipulation that optimizes action prediction during tasks. It employs an action relabeling mechanism to skip redundant steps and focus on key segments requiring precision. Extensive experiments demonstrate that SkiP can reduce execution steps by 15-40% while maintaining or improving success rates.
- ▪SkiP dynamically skips unnecessary steps in robot manipulation tasks.
- ▪The method includes a Motion Spectrum Keying procedure for automatic segmentation of actions.
- ▪Experiments show a significant reduction in executed steps without compromising success rates.
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Computer Science > Robotics arXiv:2605.15536 (cs) [Submitted on 15 May 2026] Title:SkiP: When to Skip and When to Refine for Efficient Robot Manipulation Authors:Mingtong Dai, Guanqi Peng, Yongjie Bai, Feng Yan, Chunjie Chen, Lingbo Liu, Liang Lin, Xinyu Wu View a PDF of the paper titled SkiP: When to Skip and When to Refine for Efficient Robot Manipulation, by Mingtong Dai and 7 other authors View PDF HTML (experimental) Abstract:Previous imitation learning policies predict future actions at every control step, whether in smooth motion phases or precise, contact-rich operation phases.
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