モンテカルロ木探索と教師あり学習による非歩容的脚式移動
Non-Gaited Legged Locomotion with Monte-Carlo Tree Search and Supervised Learning
サンプリングベース手法と教師あり学習を組み合わせ、脚式ロボットの歩容系列とタイミングを最適化する手法を提案し、シミュレーションと実機で有効性を検証した。
著者: Ilyass Taouil, Lorenzo Amatucci, Majid Khadiv, Angela Dai, Victor Barasuol, Giulio Turrisi, Claudio Semini
分類: cs.RO
原文アブストラクト
Legged robots are able to navigate complex terrains by continuously interacting with the environment through careful selection of contact sequences and timings. However, the combinatorial nature behind contact planning hinders the applicability of such optimization problems on hardware. In this work, we present a novel approach that optimizes gait sequences and respective timings for legged robots in the context of optimization-based controllers through the use of sampling-based methods and supervised learning techniques. We propose to bootstrap the search by learning an optimal value function in order to speed-up the gait planning procedure making it applicable in real-time. To validate our proposed method, we showcase its performance both in simulation and on hardware using a 22 kg electric quadruped robot. The method is assessed on different terrains, under external perturbations, and in comparison to a standard control approach where the gait sequence is fixed a priori.
