深層強化学習によるヒューマノイドロボットの安定した高速移動の実現
Achieving Stable High-Speed Locomotion for Humanoid Robots with Deep Reinforcement Learning
深層強化学習と運動力学的事前知識を組み合わせ、腕の協調動作で安定性を高めることで、ヒューマノイドが3.5m/sの高速移動を安定して行える制御手法を提案した。
著者: Xinming Zhang, Xianghui Wang, Lerong Zhang, Guodong Guo, Xiaoyu Shen, Wei Zhang
分類: cs.RO
原文アブストラクト
Humanoid robots offer significant versatility for performing a wide range of tasks, yet their basic ability to walk and run, especially at high velocities, remains a challenge. This letter presents a novel method that combines deep reinforcement learning with kinodynamic priors to achieve stable locomotion control (KSLC). KSLC promotes coordinated arm movements to counteract destabilizing forces, enhancing overall stability. Compared to the baseline method, KSLC provides more accurate tracking of commanded velocities and better generalization in velocity control. In simulation tests, the KSLC-enabled humanoid robot successfully tracked a target velocity of 3.5 m/s with reduced fluctuations. Sim-to-sim validation in a high-fidelity environment further confirmed its robust performance, highlighting its potential for real-world applications.
