インピーダンス整合：四足歩行ロボットにおけるRLベースの走り幅跳びの実現
Impedance Matching: Enabling an RL-Based Running Jump in a Quadruped Robot
周波数領域解析に基づくインピーダンス整合でシミュレーションと実機のギャップを埋め、四足ロボットでRLによる走り幅跳び（距離55cm、高さ38cm）を実現した。
著者: Neil Guan, Shangqun Yu, Shifan Zhu, Donghyun Kim
分類: cs.RO, cs.LG
原文アブストラクト
Replicating the remarkable athleticism seen in animals has long been a challenge in robotics control. Although Reinforcement Learning (RL) has demonstrated significant progress in dynamic legged locomotion control, the substantial sim-to-real gap often hinders the real-world demonstration of truly dynamic movements. We propose a new framework to mitigate this gap through frequency-domain analysis-based impedance matching between simulated and real robots. Our framework offers a structured guideline for parameter selection and the range for dynamics randomization in simulation, thus facilitating a safe sim-to-real transfer. The learned policy using our framework enabled jumps across distances of 55 cm and heights of 38 cm. The results are, to the best of our knowledge, one of the highest and longest running jumps demonstrated by an RL-based control policy in a real quadruped robot. Note that the achieved jumping height is approximately 85% of that obtained from a state-of-the-art trajectory optimization method, which can be seen as the physical limit for the given robot hardware. In addition, our control policy accomplished stable walking at speeds up to 2 m/s in the forward and backward directions, and 1 m/s in the sideway direction.
