HIPC: Humanoid Iterative Planning and Control via Goal-Conditioned Motion Closure
Overview
This research introduces HIPC, a real-time framework for humanoid robots that unifies generative motion planning with closed-loop control. It addresses issues like spatial drift from open-loop paradigms and discontinuities from direct state feedback by using goal-conditioned planning, deviation-decayed recovery, history re-anchoring, and latency compensation. Experiments demonstrate that HIPC achieves state-of-the-art goal-reaching accuracy while maintaining robustness against physical disturbances and the ability to recover from falls.
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- arXiv Robotics — research abstractsSignalHIPC: Humanoid Iterative Planning and Control via Goal-Conditioned Motion Closure
This research introduces HIPC, a real-time framework for humanoid robots that unifies generative motion planning with closed-loop control. It addresses issues like spatial drift from open-loop paradigms and discontinuities from direct state feedback by using goal-conditioned planning, deviation-decayed recovery, history re-anchoring, and latency compensation. Experiments demonstrate that HIPC achieves state-of-the-art goal-reaching accuracy while maintaining robustness against physical disturbances and the ability to recover from falls.
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