4 citations · 4 across the 2 of their papers we have counts for
5 papers
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.…
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…
Stabilization of Exoskeletons through Active Ankle Compensation
Thomas Gurriet, Maegan Tucker, Claudia Kann +2
This paper presents an active stabilization method for a fully actuated lower-limb exoskeleton. The method was tested on the exoskeleton ATALANTE, which was designed and built by t…
Preference-Based Learning for Exoskeleton Gait Optimization
Maegan Tucker, Ellen Novoseller, Claudia Kann +4
This paper presents a personalized gait optimization framework for lower-body exoskeletons. Rather than optimizing numerical objectives such as the mechanical cost of transport, ou…
Towards Variable Assistance for Lower Body Exoskeletons
Thomas Gurriet, Maegan Tucker, Alexis Duburcq +2
This paper presents and experimentally demonstrates a novel framework for variable assistance on lower body exoskeletons, based upon safety-critical control methods. Existing work…