デュークヒューマノイド：受動ダイナミクスを活用した省エネ二足歩行の設計と制御
The Duke Humanoid: Design and Control For Energy Efficient Bipedal Locomotion Using Passive Dynamics
10自由度のオープンソースヒューマノイドを開発し、強化学習で速度追従歩行を実現するとともに、受動ダイナミクスを活用して移動エネルギーを最大50%削減する手法を提案した。
著者: Boxi Xia, Bokuan Li, Jacob Lee, Michael Scutari, Boyuan Chen
分類: cs.RO
原文アブストラクト
We present the Duke Humanoid, an open-source 10-degrees-of-freedom humanoid, as an extensible platform for locomotion research. The design mimics human physiology, with symmetrical body alignment in the frontal plane to maintain static balance with straight knees. We develop a reinforcement learning policy that can be deployed zero-shot on the hardware for velocity-tracking walking tasks. Additionally, to enhance energy efficiency in locomotion, we propose an end-to-end reinforcement learning algorithm that encourages the robot to leverage passive dynamics. Our experimental results show that our passive policy reduces the cost of transport by up to $50\%$ in simulation and $31\%$ in real-world tests. Our website is http://generalroboticslab.com/DukeHumanoidv1/ .
