Overview

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 minimized leg distances and 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 experiment results show that our passive policy reduces the cost of transport by up to 50% in simulation and 31% in real-world testing.

Video (Click to YouTube)

Paper

Check out our paper linked here.

Open Source Hardware and Software

Check out our codebase at https://github.com/generalroboticslab/dukeHumanoid, https://github.com/generalroboticslab/dukeHumanoidHardwareControl, https://github.com/generalroboticslab/legged_env.

Citation

@misc{xia2024dukehumanoiddesigncontrol,
      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},
      year={2024},
      eprint={2409.19795},
      archivePrefix={arXiv},
      primaryClass={cs.RO},
      url={https://arxiv.org/abs/2409.19795}, 
}      

Acknowledgment

This work is supported by DARPA FoundSci HR00112490372, DARPA TIAMAT HR00112490419, ARL STRONG W911NF2320182 and W911NF2220113.

Contact

If you have any questions, please feel free to contact Boxi Xia.