Humanoid robots maintain balance and navigate by controlling the contact wrenches applied to the environment. While it is possible to plan dynamically-feasible motion that applies appropriate wrenches using existing methods, a humanoid may also be affected by external disturbances. Existing systems typically rely on controllers to reactively recover from disturbances. However, such controllers may fail when the robot cannot reach contacts capable of rejecting a given disturbance. In this paper, we propose a search-based footstep planner which aims to maximize the probability of the robot successfully reaching the goal without falling as a result of a disturbance. The planner considers not only the poses of the planned contact sequence, but also alternative contacts near the planned contact sequence that can be used to recover from external disturbances. Although this additional consideration significantly increases the computation load, we train neural networks to efficiently predict multi-contact zero-step and one-step capturability, which allows the planner to generate robust contact sequences efficiently. Our results show that our approach generates footstep sequences that are more robust to external disturbances than a conventional footstep planner in four challenging scenarios.

中文翻译：

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具有学习的零步和一步可捕获性预测的稳健人形接触规划

人形机器人通过控制应用于环境的接触扳手来保持平衡和导航。虽然可以使用现有方法来规划应用适当扳手的动态可行运动，但人形机器人也可能受到外部干扰的影响。现有系统通常依靠控制器从干扰中被动恢复。然而，当机器人无法接触到能够拒绝给定干扰的触点时，这种控制器可能会失效。在本文中，我们提出了一种基于搜索的足迹规划器，旨在最大化机器人成功到达目标而不会因干扰而跌倒的概率。规划者不仅要考虑规划接触序列的姿态，以及计划接触序列附近的替代接触，可用于从外部干扰中恢复。尽管这种额外的考虑显着增加了计算负载，但我们训练神经网络来有效地预测多接触零步和一步可捕获性，这使规划器能够有效地生成稳健的接触序列。我们的结果表明，在四种具有挑战性的场景中，我们的方法生成的足迹序列比传统的足迹规划器对外部干扰更稳健。