学習による歩容遷移で実現する高速・高効率な四脚ロボットの移動
Fast and Efficient Locomotion via Learned Gait Transitions
エネルギー最小化の報酬だけで歩容と歩容遷移が自動獲得される階層型学習フレームワークを提案し、四脚ロボットで歩行からトロット、フライトロットへの遷移を実現した。
著者: Yuxiang Yang, Tingnan Zhang, Erwin Coumans, Jie Tan, Byron Boots
分類: cs.RO, cs.LG
原文アブストラクト
We focus on the problem of developing energy efficient controllers for quadrupedal robots. Animals can actively switch gaits at different speeds to lower their energy consumption. In this paper, we devise a hierarchical learning framework, in which distinctive locomotion gaits and natural gait transitions emerge automatically with a simple reward of energy minimization. We use evolutionary strategies (ES) to train a high-level gait policy that specifies gait patterns of each foot, while the low-level convex MPC controller optimizes the motor commands so that the robot can walk at a desired velocity using that gait pattern. We test our learning framework on a quadruped robot and demonstrate automatic gait transitions, from walking to trotting and to fly-trotting, as the robot increases its speed. We show that the learned hierarchical controller consumes much less energy across a wide range of locomotion speed than baseline controllers.
