  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.
