HIPC: Humanoid Iterative Planning and Control via Goal-Conditioned Motion Closure
HIPC: 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.
The paper proposes HIPC, a real-time framework combining generative motion models with closed-loop control to address tracking drift and disturbance recovery in humanoid robots, validated through experiments on physical perturbations and fall recovery.
Source: arXiv Robotics — research abstracts · Read original article ↗
Loading article text…
What the source reports
Publisher-reported claims, with original evidence. These results have not been independently verified by RoboSignal.
- paper: not_reported
Source excerpts and review record
Automatically extracted; no manual editorial approval recorded.
Source：arXiv Robotics — research abstracts · arxiv.org