脚式マニピュレータによるカート押し学習のための部分動作模倣
Partial Motion Imitation for Learning Cart Pushing with Legged Manipulators
脚式ロボットの移動操作タスクにおいて、歩行タスクから学習した下半身の動作のみを模倣する部分敵対的動作事前分布を用いた部分模倣学習を提案し、カート押し操作を安定かつ正確に学習できることを示した。
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著者: Mili Das, Morgan Byrd, Donghoon Baek, Sehoon Ha
分類: cs.RO
原文アブストラクト
Loco-manipulation is a key capability for legged robots to perform practical mobile manipulation tasks, such as transporting and pushing objects, in real-world environments. However, learning robust loco-manipulation skills remains challenging due to the difficulty of maintaining stable locomotion while simultaneously performing precise manipulation behaviors. This work proposes a partial imitation learning approach that transfers the locomotion style learned from a locomotion task to cart loco-manipulation. A robust locomotion policy is first trained with extensive domain and terrain randomization, and a loco-manipulation policy is then learned by imitating only lower-body motions using a partial adversarial motion prior. We conduct experiments demonstrating that the learned policy successfully pushes a cart along diverse trajectories in IsaacLab and transfers effectively to MuJoCo. We also compare our method to several baselines and show that the proposed approach achieves more stable and accurate loco-manipulation behaviors.
