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.
翻译:描述用户喜欢的外骨骼学习类型, 并理解更普遍的行走科学, 需要恢复用户的实用环境。 学习这些景观具有挑战性, 因为行走轨迹是由许多步步参数定义的, 人类试验的数据收集费用昂贵, 并且必须确保用户的安全和舒适。 这项工作提出了“ 兴趣区域主动学习( ROIAL) 框架 ”, 积极了解每个用户对一个利益区域的基本实用功能, 以确保安全和舒适。 ROIAL 学习星系和偏好反馈, 这些反馈比绝对数字分更可靠的反馈机制。 算法的性能在模拟和实验中评价三个非残疾人在较低体外骨骼内行走的成绩。 ROIAL 学习Bayesian 海报, 预测每个外骨骼用户在四个Exoskeleton gait 参数上的实用环境。 算法发现用户的喜好有共性和差异, 并确定了最有影响的用户反馈的网格参数。 这些结果显示从有限的人类试验中恢复网格实用环境的可行性。