Learning Controller Gains on Bipedal Walking Robots via User Preferences

TL;DR

This paper demonstrates how preference-based learning can optimize controller gains for bipedal robots, enabling stable and robust locomotion without requiring deep domain expertise.

Contribution

It introduces a novel preference-based online learning method to tune nonlinear controller gains for bipedal robots, reducing reliance on expert knowledge.

Findings

Successfully applied to planar and 3D bipeds

Achieved stable and robust walking behaviors

Demonstrated repeatability and effectiveness of the method

Abstract

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.