In this paper, we propose a locomotion training framework where a control policy and a state estimator are trained concurrently. The framework consists of a policy network which outputs the desired joint positions and a state estimation network which outputs estimates of the robot's states such as the base linear velocity, foot height, and contact probability. We exploit a fast simulation environment to train the networks and the trained networks are transferred to the real robot. The trained policy and state estimator are capable of traversing diverse terrains such as a hill, slippery plate, and bumpy road. We also demonstrate that the learned policy can run at up to 3.75 m/s on normal flat ground and 3.54 m/s on a slippery plate with the coefficient of friction of 0.22.
@article{arxiv.2202.05481,
title = {Concurrent Training of a Control Policy and a State Estimator for Dynamic and Robust Legged Locomotion},
author = {Gwanghyeon Ji and Juhyeok Mun and Hyeongjun Kim and Jemin Hwangbo},
journal= {arXiv preprint arXiv:2202.05481},
year = {2022}
}
Comments
Accepted for IEEE Robotics and Automation Letters (RA-L) and ICRA 2022