Learning Controller Gains on Bipedal Walking Robots via User Preferences
This addresses the challenge of manually tuning controller gains for complex robotic behaviors, which is time-consuming and requires expert knowledge, though it is incremental as it applies existing preference-based learning to a specific domain.
The paper tackles the problem of optimizing controller gains for bipedal walking robots by using preference-based learning to query user preferences online, and demonstrates stable and robust locomotion on two underactuated bipedal robots, AMBER and Cassie.
Experimental demonstration of complex robotic behaviors relies heavily on finding the correct controller gains. This painstaking process is often completed by a domain expert, requiring deep knowledge of the relationship between parameter values and the resulting behavior of the system. Even when such knowledge is possessed, it can take significant effort to navigate the nonintuitive landscape of possible parameter combinations. In this work, we explore the extent to which preference-based learning can be used to optimize controller gains online by repeatedly querying the user for their preferences. This general methodology is applied to two variants of control Lyapunov function based nonlinear controllers framed as quadratic programs, which provide theoretical guarantees but are challenging to realize in practice. These controllers are successfully demonstrated both on the planar underactuated biped, AMBER, and on the 3D underactuated biped, Cassie. We experimentally evaluate the performance of the learned controllers and show that the proposed method is repeatably able to learn gains that yield stable and robust locomotion.