This paper wins both the ICRA 2020 Best Paper Award and the Best Paper in Human-Robot Interaction Award!
This video illustrates preference-based learning for exoskeleton gait optimization. This work appears at ICRA 2020.
Additional details can be found in the associated paper: https://arxiv.org/abs/1909.12316
Authors: Tucker, Maegan; Novoseller, Ellen; Kann, Claudia; Sui, Yanan; Yue, Yisong; Burdick, Joel; Ames, Aaron
This paper presents a personalized gait optimization framework for lower-body exoskeletons. Rather than optimizing numerical objectives such as the mechanical cost of transport, our approach directly learns from user preferences, e.g., for comfort. Building upon work in preference-based interactive learning, we present the CoSpar algorithm. CoSpar prompts the user to give pairwise preferences between trials and suggest improvements; as exoskeleton walking is a non-intuitive behavior, users can provide preferences more easily and reliably than numerical feedback. We show that CoSpar performs competitively in simulation and demonstrate a prototype implementation of CoSpar on a lower-body exoskeleton to optimize human walking trajectory features. In the experiments, CoSpar consistently found user-preferred parameters of the exoskeleton's walking gait, which suggests that it is a promising starting point for adapting and personalizing exoskeletons (or other assistive devices) to individual users.