LAGOON: 言語指示によるロボット動作制御
LAGOON: Language-Guided Motion Control
言語コマンドから人間の動作を生成し、強化学習とドメインランダム化で四足歩行ロボットに転移させる手法を開発した。
著者: Shusheng Xu, Huaijie Wang, Jiaxuan Gao, Yutao Ouyang, Chao Yu, Yi Wu
分類: cs.RO
原文アブストラクト
We aim to control a robot to physically behave in the real world following any high-level language command like "cartwheel" or "kick". Although human motion datasets exist, this task remains particularly challenging since generative models can produce physically unrealistic motions, which will be more severe for robots due to different body structures and physical properties. Deploying such a motion to a physical robot can cause even greater difficulties due to the sim2real gap. We develop LAnguage-Guided mOtion cONtrol (LAGOON), a multi-phase reinforcement learning (RL) method to generate physically realistic robot motions under language commands. LAGOON first leverages a pretrained model to generate a human motion from a language command. Then an RL phase trains a control policy in simulation to mimic the generated human motion. Finally, with domain randomization, our learned policy can be deployed to a quadrupedal robot, leading to a quadrupedal robot that can take diverse behaviors in the real world under natural language commands
