離散時間ハイブリッドオートマタ学習：脚式ロコモーションとスケートボードの融合
Discrete-Time Hybrid Automata Learning: Legged Locomotion Meets Skateboarding
軌道分割やイベント関数なしでモード切替を学習する枠組みDHALを提案し、強化学習に組み込んで四足ロボットのスケートボードという難タスクを実現した。
著者: Hang Liu, Sangli Teng, Ben Liu, Wei Zhang, Maani Ghaffari
分類: cs.RO
原文アブストラクト
Hybrid dynamical systems, which include continuous flow and discrete mode switching, can model robotics tasks like legged robot locomotion. Model-based methods usually depend on predefined gaits, while model-free approaches lack explicit mode-switching knowledge. Current methods identify discrete modes via segmentation before regressing continuous flow, but learning high-dimensional complex rigid body dynamics without trajectory labels or segmentation is a challenging open problem. This paper introduces Discrete-time Hybrid Automata Learning (DHAL), a framework to identify and execute mode-switching without trajectory segmentation or event function learning. Besides, we embedded it in reinforcement learning pipeline and incorporates a beta policy distribution and a multi-critic architecture to model contact-guided motions, exemplified by a challenging quadrupedal robot skateboard task. We validate our method through sufficient real-world tests, demonstrating robust performance and mode identification consistent with human intuition in hybrid dynamical systems.
