Preference-Based Learning for User-Guided HZD Gait Generation on Bipedal Walking Robots

TL;DR

This paper introduces a novel framework combining control theory and preference-based learning to generate stable, robust, and natural bipedal walking gaits on robots without manual tuning or simulation reliance.

Contribution

It presents a new approach that integrates hybrid zero dynamics optimization with human preference-based learning, eliminating the need for carefully crafted reward functions.

Findings

Achieved stable walking in fewer than 50 iterations

Demonstrated robustness with added model uncertainty

Generated natural gait without simulation or manual tuning

Abstract

This paper presents a framework that leverages both control theory and machine learning to obtain stable and robust bipedal locomotion without the need for manual parameter tuning. Traditionally, gaits are generated through trajectory optimization methods and then realized experimentally -- a process that often requires extensive tuning due to differences between the models and hardware. In this work, the process of gait realization via hybrid zero dynamics (HZD) based optimization is formally combined with preference-based learning to systematically realize dynamically stable walking. Importantly, this learning approach does not require a carefully constructed reward function, but instead utilizes human pairwise preferences. The power of the proposed approach is demonstrated through two experiments on a planar biped AMBER-3M: the first with rigid point-feet, and the second with induced model uncertainty through the addition of springs where the added compliance was not accounted for in the gait generation or in the controller. In both experiments, the framework achieves stable, robust, efficient, and natural walking in fewer than 50 iterations with no reliance on a simulation environment. These results demonstrate a promising step in the unification of control theory and learning.