学習した行動残差による連続的な多様ジャンプ
Continuous Versatile Jumping Using Learned Action Residuals
最適制御と強化学習を組み合わせた階層的枠組みで、四足ロボットがシミュレーションから実機へ直接展開でき、最大50cmの高さ・60cmの前方・90度の旋回を含む多様で連続的なジャンプ動作を実現した。
著者: Yuxiang Yang, Xiangyun Meng, Wenhao Yu, Tingnan Zhang, Jie Tan, Byron Boots
分類: cs.RO, cs.AI, cs.LG
原文アブストラクト
Jumping is essential for legged robots to traverse through difficult terrains. In this work, we propose a hierarchical framework that combines optimal control and reinforcement learning to learn continuous jumping motions for quadrupedal robots. The core of our framework is a stance controller, which combines a manually designed acceleration controller with a learned residual policy. As the acceleration controller warm starts policy for efficient training, the trained policy overcomes the limitation of the acceleration controller and improves the jumping stability. In addition, a low-level whole-body controller converts the body pose command from the stance controller to motor commands. After training in simulation, our framework can be deployed directly to the real robot, and perform versatile, continuous jumping motions, including omni-directional jumps at up to 50cm high, 60cm forward, and jump-turning at up to 90 degrees. Please visit our website for more results: https://sites.google.com/view/learning-to-jump.
