1 research outputs found
RL + Model-based Control: Using On-demand Optimal Control to Learn Versatile Legged Locomotion
This letter presents a versatile control method for dynamic and robust legged
locomotion that integrates model-based optimal control with reinforcement
learning (RL). Our approach involves training an RL policy to imitate reference
motions generated on-demand through solving a finite-horizon optimal control
problem. This integration enables the policy to leverage human expertise in
generating motions to imitate while also allowing it to generalize to more
complex scenarios that require a more complex dynamics model. Our method
successfully learns control policies capable of generating diverse quadrupedal
gait patterns and maintaining stability against unexpected external
perturbations in both simulation and hardware experiments. Furthermore, we
demonstrate the adaptability of our method to more complex locomotion tasks on
uneven terrain without the need for excessive reward shaping or hyperparameter
tuning.Comment: 8 pages. 8 figures. The supplementary video is available in
https://youtu.be/gXDP87yVq4