  Optimizing lower-body exoskeleton walking gaits for user comfort requires
understanding users' preferences over a high-dimensional gait parameter space.
However, existing preference-based learning methods have only explored
low-dimensional domains due to computational limitations. To learn user
preferences in high dimensions, this work presents LineCoSpar, a
human-in-the-loop preference-based framework that enables optimization over
many parameters by iteratively exploring one-dimensional subspaces.
Additionally, this work identifies gait attributes that characterize broader
preferences across users. In simulations and human trials, we empirically
verify that LineCoSpar is a sample-efficient approach for high-dimensional
preference optimization. Our analysis of the experimental data reveals a
correspondence between human preferences and objective measures of dynamicity,
while also highlighting differences in the utility functions underlying
individual users' gait preferences. This result has implications for
exoskeleton gait synthesis, an active field with applications to clinical use
and patient rehabilitation.
