Sim-to-Real強化学習による二足歩行ロボットの動的機動
Dynamic Bipedal Maneuvers through Sim-to-Real Reinforcement Learning
最適化された単剛体モデルの軌道を参照データとして再帰的方策を訓練し、Cassieで4歩での90度旋回を実機実現した。
著者: Fangzhou Yu, Ryan Batke, Jeremy Dao, Jonathan Hurst, Kevin Green, Alan Fern
分類: cs.RO
原文アブストラクト
For legged robots to match the athletic capabilities of humans and animals, they must not only produce robust periodic walking and running, but also seamlessly switch between nominal locomotion gaits and more specialized transient maneuvers. Despite recent advancements in controls of bipedal robots, there has been little focus on producing highly dynamic behaviors. Recent work utilizing reinforcement learning to produce policies for control of legged robots have demonstrated success in producing robust walking behaviors. However, these learned policies have difficulty expressing a multitude of different behaviors on a single network. Inspired by conventional optimization-based control techniques for legged robots, this work applies a recurrent policy to execute four-step, 90 degree turns trained using reference data generated from optimized single rigid body model trajectories. We present a novel training framework using epilogue terminal rewards for learning specific behaviors from pre-computed trajectory data and demonstrate a successful transfer to hardware on the bipedal robot Cassie.
