形態と制御を考慮した強化学習による汎化可能な四足歩行
McARL:Morphology-Control-Aware Reinforcement Learning for Generalizable Quadrupedal Locomotion
形態ベクトルを条件にした方策を強化学習で学習し、再学習なしで異なる四足ロボットへゼロショット転移できる手法を提案。
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著者: Prakhar Mishra, Amir Hossain Raj, Xuesu Xiao, Dinesh Manocha
分類: cs.RO
原文アブストラクト
We present Morphology-Control-Aware Reinforcement Learning (McARL), a new approach to overcome challenges of hyperparameter tuning and transfer loss, enabling generalizable locomotion across robot morphologies. We use a morphology-conditioned policy by incorporating a randomized morphology vector, sampled from a defined morphology range, into both the actor and critic networks. This allows the policy to learn parameters that generalize to robots with similar characteristics. We demonstrate that a single policy trained on a Unitree Go1 robot using McARL can be transferred to a different morphology (e.g., Unitree Go2 robot) and can achieve zero-shot transfer velocity of up to 3.5 m/s without retraining or fine-tuning. Moreover, it achieves 6.0 m/s on the training Go1 robot and generalizes to other morphologies like A1 and Mini Cheetah. We also analyze the impact of morphology distance on transfer performance and highlight McARL's advantages over prior approaches. McARL achieves 44-150% higher transfer performance on Go2, Mini Cheetah, and A1 compared to PPO variants.
