  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. Learning these landscapes is challenging, as walking
trajectories are defined by numerous gait parameters, data collection from
human trials is expensive, and user safety and comfort must be ensured. This
work proposes the Region of Interest Active Learning (ROIAL) framework, which
actively learns each user's underlying utility function over a region of
interest that ensures safety and comfort. ROIAL learns from ordinal and
preference feedback, which are more reliable feedback mechanisms than absolute
numerical scores. The algorithm's performance is evaluated both in simulation
and experimentally for three non-disabled subjects walking inside of a
lower-body exoskeleton. ROIAL learns Bayesian posteriors that predict each
exoskeleton user's utility landscape across four exoskeleton gait parameters.
The algorithm discovers both commonalities and discrepancies across users' gait
preferences and identifies the gait parameters that most influenced user
feedback. These results demonstrate the feasibility of recovering gait utility
landscapes from limited human trials.
