多様な脚式ロボットを制御する単一ポリシー：エンドツーエンド学習によるマルチエンボディメント歩行
One Policy to Run Them All: an End-to-end Learning Approach to Multi-Embodiment Locomotion
形態に依存しないエンコーダ・デコーダを備えた統合ロボット形態アーキテクチャURMAを提案し、四足・人型・六脚など異なる脚式ロボットを単一の歩行ポリシーで制御、未見の機体にもゼロ/少数ショット転移できることを示した。
著者: Nico Bohlinger, Grzegorz Czechmanowski, Maciej Krupka, Piotr Kicki, Krzysztof Walas, Jan Peters, Davide Tateo
分類: cs.RO, cs.LG
原文アブストラクト
Deep Reinforcement Learning techniques are achieving state-of-the-art results in robust legged locomotion. While there exists a wide variety of legged platforms such as quadruped, humanoids, and hexapods, the field is still missing a single learning framework that can control all these different embodiments easily and effectively and possibly transfer, zero or few-shot, to unseen robot embodiments. We introduce URMA, the Unified Robot Morphology Architecture, to close this gap. Our framework brings the end-to-end Multi-Task Reinforcement Learning approach to the realm of legged robots, enabling the learned policy to control any type of robot morphology. The key idea of our method is to allow the network to learn an abstract locomotion controller that can be seamlessly shared between embodiments thanks to our morphology-agnostic encoders and decoders. This flexible architecture can be seen as a potential first step in building a foundation model for legged robot locomotion. Our experiments show that URMA can learn a locomotion policy on multiple embodiments that can be easily transferred to unseen robot platforms in simulation and the real world.
