ROIAL swMATH ID: 39824 Software Authors: Kejun Li, Maegan Tucker, Erdem Bıyık, Ellen Novoseller, Joel W. Burdick, Yanan Sui, Dorsa Sadigh, Yisong Yue, Aaron D. Ames Description: ROIAL: Region of Interest Active Learning for Characterizing Exoskeleton Gait Preference Landscapes. 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. Homepage: https://arxiv.org/abs/2011.04812 Source Code: https://github.com/kli58/ROIAL Keywords: Robotics; arXiv_cs.RO; Human-Computer Interaction; arXiv_cs.HC; Machine Learning; arXiv_cs.LG; exoskeleton gaits Related Software: SciPy; NumPy; OpenAI Gym; Python; APReL Cited in: 0 Publications