36 citations · 47 across the 23 of their papers we have counts for
3 papers · 1 filter
Preference-Based Learning for User-Guided HZD Gait Generation on Bipedal Walking Robots
Maegan Tucker, Noel Csomay-Shanklin, Wen-Loong Ma +1
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.…
ROIAL: Region of Interest Active Learning for Characterizing Exoskeleton Gait Preference Landscapes
Kejun Li, Maegan Tucker, Erdem Bıyık +6
Characterizing what types of exoskeleton gaits are comfortable for users, and understanding the science of walking more generally, require recovering a user's utility landscape. Le…
Human Preference-Based Learning for High-dimensional Optimization of Exoskeleton Walking Gaits
Maegan Tucker, Myra Cheng, Ellen Novoseller +4
Optimizing lower-body exoskeleton walking gaits for user comfort requires understanding users' preferences over a high-dimensional gait parameter space. However, existing preferenc…