SkiP: When to Skip and When to Refine for Efficient Robot Manipulation

• URL: http://arxiv.org/abs/2605.15536v1
• Date: Fri, 15 May 2026 02:16:34 GMT
• Title: SkiP: When to Skip and When to Refine for Efficient Robot Manipulation
• Abstract summary: textbfSkip Policy (SkiP) dynamically leaps over skip segments and intensively refines actions in key segments.<n>emphMotion Spectrum Keying (MSK) is a fast, task-agnostic procedure that detects local motion complexity from action signals.<n>Experiments show SkiP reduces executed steps by $15$--$40%$ while matching or improving success rates across various policy backbones.
• Score: 50.03201984643502
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: 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 \textbf{Skip Policy (SkiP)} dynamically leaps over skip segments and intensively refines actions in key segments, within a single unified network requiring no learned skip planner or hierarchical structure. To automatically partition demonstrations into key and skip segments without manual annotation, we introduce \emph{Motion Spectrum Keying} (MSK), a fast, task-agnostic procedure that detects local motion complexity from action signals. Extensive experiments across 72 simulated manipulation tasks and three real-robot tasks show that SkiP reduces executed steps by $15$--$40\%$ while matching or improving success rates across various policy backbones. Project page: \texttt{https://pgq18.github.io/SkiP-page/}.

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