Preference-Based Learning for Exoskeleton Gait Optimization
This addresses the challenge of adapting exoskeletons to individual users, which is incremental as it builds on existing preference-based interactive learning methods.
The paper tackled the problem of personalizing exoskeleton gaits by developing a preference-based learning framework that directly learns from user preferences like comfort, rather than numerical objectives. It introduced the CoSpar algorithm, which performed competitively in simulation and in a prototype implementation, consistently finding user-preferred parameters for exoskeleton walking gaits.
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.