強化学習による敏捷で汎用的な二足歩行ロボットの追従制御
Agile and versatile bipedal robot tracking control through reinforcement learning
単一の小規模ニューラルネットワークとモデルベースIK・強化学習を組み合わせ、足首と体幹の軌道追従を幅広い歩容で実現する二足歩行ロボットの汎用コントローラを提案した。
著者: Jiayi Li, Linqi Ye, Yi Cheng, Houde Liu, Bin Liang
分類: cs.RO, cs.LG
原文アブストラクト
The remarkable athletic intelligence displayed by humans in complex dynamic movements such as dancing and gymnastics suggests that the balance mechanism in biological beings is decoupled from specific movement patterns. This decoupling allows for the execution of both learned and unlearned movements under certain constraints while maintaining balance through minor whole-body coordination. To replicate this balance ability and body agility, this paper proposes a versatile controller for bipedal robots. This controller achieves ankle and body trajectory tracking across a wide range of gaits using a single small-scale neural network, which is based on a model-based IK solver and reinforcement learning. We consider a single step as the smallest control unit and design a universally applicable control input form suitable for any single-step variation. Highly flexible gait control can be achieved by combining these minimal control units with high-level policy through our extensible control interface. To enhance the trajectory-tracking capability of our controller, we utilize a three-stage training curriculum. After training, the robot can move freely between target footholds at varying distances and heights. The robot can also maintain static balance without repeated stepping to adjust posture. Finally, we evaluate the tracking accuracy of our controller on various bipedal tasks, and the effectiveness of our control framework is verified in the simulation environment.
