学習した重心ダイナミクス予測による効率的なヒューマノイド接触計画
Efficient Humanoid Contact Planning using Learned Centroidal Dynamics Prediction
重心モーメンタムの動的進化を学習で予測し、探索ベースの接触計画で動的にロバストな接触系列を高速生成する手法を提案した。
著者: Yu-Chi Lin, Brahayam Ponton, Ludovic Righetti, Dmitry Berenson
分類: cs.RO
原文アブストラクト
Humanoid robots dynamically navigate an environment by interacting with it via contact wrenches exerted at intermittent contact poses. Therefore, it is important to consider dynamics when planning a contact sequence. Traditional contact planning approaches assume a quasi-static balance criterion to reduce the computational challenges of selecting a contact sequence over a rough terrain. This however limits the applicability of the approach when dynamic motions are required, such as when walking down a steep slope or crossing a wide gap. Recent methods overcome this limitation with the help of efficient mixed integer convex programming solvers capable of synthesizing dynamic contact sequences. Nevertheless, its exponential-time complexity limits its applicability to short time horizon contact sequences within small environments. In this paper, we go beyond current approaches by learning a prediction of the dynamic evolution of the robot centroidal momenta, which can then be used for quickly generating dynamically robust contact sequences for robots with arms and legs using a search-based contact planner. We demonstrate the efficiency and quality of the results of the proposed approach in a set of dynamically challenging scenarios.
