学習したゼロ・ワンステップ捕捉性予測による堅牢なヒューマノイド接触計画
Robust Humanoid Contact Planning with Learned Zero- and One-Step Capturability Prediction
外乱を受けても転倒せず目標に到達できる確率を最大化する足配置計画を提案し、ニューラルネットでゼロ・ワンステップ捕捉性を高速予測することで、従来より外乱に強い計画を効率的に生成できることを示した。
著者: Yu-Chi Lin, Ludovic Righetti, Dmitry Berenson
分類: cs.RO
原文アブストラクト
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.
