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arXiv · 2610.00823

Reactive Humanoid Multi-Contact Using Learned Stability Models

Stephen McCrory·Beomyeong Park·Nicholas Kitchel·Nehar Poddar·Robert Griffin
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Abstract

We present a planning and control approach to reactively use hand contacts to stabilize a humanoid in low stability scenarios, where only using feet contacts may result in a fall. Candidate contacts are sampled within the robot's reachable workspace, and a preview is computed by rolling out the centroidal dynamics through pre-impact, impact and post-impact phases. Sampled points are scored based on the Center of Pressure (CoP) control authority at the post-impact phase. Central to our approach is a learned model of the robot's CoP region during post-impact, which enables rapid evaluation of candidate contact points compared to traditional optimization-based methods. The presented planner has two stages: the first selects an optimal bracing region and the second computes an optimal bracing point within the region. Our simulation results demonstrate an average increase in impulse resilience of 89% over recovery without hand contacts and 17% over a naive planning strategy (closest reachable region). We validate our framework on hardware, performing push tests while standing and walking. The standing trials show an average 43% reduction in stabilization time compared to naive hand placement and the walking trials demonstrate a 18% reduction compared to baseline recovery (without hand contacts).

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Stephen McCrory, Beomyeong Park, Nicholas Kitchel, Nehar Poddar, Robert Griffin. 2026-09-30. Reactive Humanoid Multi-Contact Using Learned Stability Models. https://arxiv.org/abs/2610.00823

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