外观
论文arXiv 2610.0082328 天前
基于学习稳定性模型的反应式人形机器人多接触
原文链接：Reactive Humanoid Multi-Contact Using Learned Stability Models（arXiv 2610.00823 · 发布日期：2026-09-30）
Reactive Humanoid Multi-Contact Using Learned Stability Models
针对低稳定场景下足接触易跌倒，方法学习冲击后CoP区域规划手部支撑，并在仿真和真机站立、行走推搡测试中验证。
核心要点
- 低稳定场景中，仅依靠足部接触恢复平衡可能导致人形机器人跌倒。
- 在可达工作空间采样候选手接触，并滚动质心动力学预览冲击前后过程。
- 学习冲击后CoP区域以快速评估接触点，并分两阶段选择支撑区域与支撑点。
- 仿真平均冲量抗扰性提升89%和17%；真机站立稳定时间降43%，行走降18%。
验证范围与局限：验证范围为仿真以及真机站立和行走时的推搡测试。
英文摘要 (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).
作者：Stephen McCrory, Beomyeong Park, Nicholas Kitchel, Nehar Poddar, Robert Griffin
来源
本页事实对应的公开页面，以原文为准。