敵対的モーションプライアを用いた歩行と飛行の学習
Learning to Walk and Fly with Adversarial Motion Priors
敵対的モーションプライアを活用し、脚式ロボットが歩行と飛行を滑らかに切り替えるマルチモーダル移動を強化学習で獲得する手法を提案した。
著者: Giuseppe L'Erario, Drew Hanover, Angel Romero, Yunlong Song, Gabriele Nava, Paolo Maria Viceconte, Daniele Pucci, Davide Scaramuzza
分類: cs.RO
原文アブストラクト
Robot multimodal locomotion encompasses the ability to transition between walking and flying, representing a significant challenge in robotics. This work presents an approach that enables automatic smooth transitions between legged and aerial locomotion. Leveraging the concept of Adversarial Motion Priors, our method allows the robot to imitate motion datasets and accomplish the desired task without the need for complex reward functions. The robot learns walking patterns from human-like gaits and aerial locomotion patterns from motions obtained using trajectory optimization. Through this process, the robot adapts the locomotion scheme based on environmental feedback using reinforcement learning, with the spontaneous emergence of mode-switching behavior. The results highlight the potential for achieving multimodal locomotion in aerial humanoid robotics through automatic control of walking and flying modes, paving the way for applications in diverse domains such as search and rescue, surveillance, and exploration missions. This research contributes to advancing the capabilities of aerial humanoid robots in terms of versatile locomotion in various environments.
