ROIAL: Region of Interest Active Learning for Characterizing Exoskeleton Gait Preference Landscapes
This work addresses the challenge of safely and efficiently characterizing user preferences for exoskeleton gaits, which is crucial for personalized assistive technology, though it is incremental as it builds on active learning methods.
The paper tackled the problem of learning exoskeleton users' gait utility landscapes from limited and expensive human trials, proposing the ROIAL framework that actively learns these landscapes using ordinal and preference feedback, and demonstrated its feasibility by recovering landscapes for three non-disabled subjects across four gait parameters.
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.