Preference-Based Learning for User-Guided HZD Gait Generation on Bipedal Walking Robots
Maegan Tucker,Noel Csomay-Shanklin,Wen-Loong Ma,Aaron D. Ames,Maegan Tucker,Noel Csomay-Shanklin,Wen-Loong Ma,Aaron D. Ames
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 wo...
