杜克仿人机器人通过被动动力学实现节能行走，实测能耗降低31%。
The Duke Humanoid: Design and Control For Energy Efficient Bipedal Locomotion Using Passive Dynamics
- 模仿人体结构设计，用被动动力学减少主动驱动需求。
- 仿真中能耗降低50%，真实行走中降低31%。
- 开源平台适合机器人步态研究，尤其关注能效优化者。
我们介绍杜克仿人机器人（Duke Humanoid），一款10自由度的开源仿人机器人平台，用于步态研究。其设计模仿人体生理结构，前后对称布局，可保持膝关节伸直时的静态平衡。我们开发了一种强化学习策略，可在硬件上零样本部署，实现速度跟踪行走。为提升运动能效，提出端到端强化学习算法，鼓励机器人利用被动动力学。实验表明，该被动策略在仿真中使运输成本降低50%，真实测试中降低31%。项目主页：http://generalroboticslab.com/DukeHumanoidv1/
原文摘要 · Abstract (English)
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/ .
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