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 in simulation and in real-world tests. Our website isthis http URL.
View on arXiv@article{xia2025_2409.19795,
title={ The Duke Humanoid: Design and Control For Energy Efficient Bipedal Locomotion Using Passive Dynamics },
author={ Boxi Xia and Bokuan Li and Jacob Lee and Michael Scutari and Boyuan Chen },
journal={arXiv preprint arXiv:2409.19795},
year={ 2025 }
}