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This paper presents a framework that leverages both control theory andmachine learning to obtain stable and robust bipedal locomotion without theneed for manual parameter tuning. Traditionally, gaits are generated throughtrajectory optimization methods and then realized experimentally -- a processthat often requires extensive tuning due to differences between the models andhardware. In this work, the process of gait realization via hybrid zerodynamics (HZD) based optimization is formally combined with preference-basedlearning to systematically realize dynamically stable walking. Importantly,this learning approach does not require a carefully constructed rewardfunction, but instead utilizes human pairwise preferences. The power of theproposed approach is demonstrated through two experiments on a planar bipedAMBER-3M: the first with rigid point-feet, and the second with induced modeluncertainty through the addition of springs where the added compliance was notaccounted for in the gait generation or in the controller. In both experiments,the framework achieves stable, robust, efficient, and natural walking in fewerthan 50 iterations with no reliance on a simulation environment. These resultsdemonstrate a promising step in the unification of control theory and learning.

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