Continuous Versatile Jumping Using Learned Action Residuals
Abstract
Jumping is essential for legged robots to traverse through difficult terrains. In this work, we propose a hierarchical framework that combines optimal control and reinforcement learning to learn continuous jumping motions for quadrupedal robots. The core of our framework is the high-level stance controller, which combines a manually designed acceleration controller with a learned residual policy. As the acceleration controller warm starts policy for efficient and smooth training, the trained policy improves the overall jumping stability beyond the controller’s limitations. In addition, a low-level whole-body controller converts the body pose command from the stance controller to motor actions. After training in simulation, our framework can be deployed directly to the real robot, and perform versatile, continuous jumping motions, including omni-directional jumps at up to 50cm high, 60cm forward, and jump-turning at up to 90 degrees. Please visit our website for more results: https://sites.google.com/view/learning-to-jump.
Cite
Text
Yang et al. "Continuous Versatile Jumping Using Learned Action Residuals." Proceedings of The 5th Annual Learning for Dynamics and Control Conference, 2023.Markdown
[Yang et al. "Continuous Versatile Jumping Using Learned Action Residuals." Proceedings of The 5th Annual Learning for Dynamics and Control Conference, 2023.](https://mlanthology.org/l4dc/2023/yang2023l4dc-continuous/)BibTeX
@inproceedings{yang2023l4dc-continuous,
title = {{Continuous Versatile Jumping Using Learned Action Residuals}},
author = {Yang, Yuxiang and Meng, Xiangyun and Yu, Wenhao and Zhang, Tingnan and Tan, Jie and Boots, Byron},
booktitle = {Proceedings of The 5th Annual Learning for Dynamics and Control Conference},
year = {2023},
pages = {770-782},
volume = {211},
url = {https://mlanthology.org/l4dc/2023/yang2023l4dc-continuous/}
}