DeepGait: 深層強化学習による四脚歩容の計画と制御
DeepGait: Planning and Control of Quadrupedal Gaits using Deep Reinforcement Learning
モデルベース計画と強化学習を組み合わせ、動的実現可能性の評価をMDPに組み込むことで、不整地における足場と胴体の運動を計画・実行する四脚ロボットの歩容制御ポリシーを学習した。
著者: Vassilios Tsounis, Mitja Alge, Joonho Lee, Farbod Farshidian, Marco Hutter
分類: cs.RO, cs.LG
原文アブストラクト
This paper addresses the problem of legged locomotion in non-flat terrain. As legged robots such as quadrupeds are to be deployed in terrains with geometries which are difficult to model and predict, the need arises to equip them with the capability to generalize well to unforeseen situations. In this work, we propose a novel technique for training neural-network policies for terrain-aware locomotion, which combines state-of-the-art methods for model-based motion planning and reinforcement learning. Our approach is centered on formulating Markov decision processes using the evaluation of dynamic feasibility criteria in place of physical simulation. We thus employ policy-gradient methods to independently train policies which respectively plan and execute foothold and base motions in 3D environments using both proprioceptive and exteroceptive measurements. We apply our method within a challenging suite of simulated terrain scenarios which contain features such as narrow bridges, gaps and stepping-stones, and train policies which succeed in locomoting effectively in all cases.
