Human-in-the-Loop Pareto Optimization: Trade-Off Characterization for Assist-as-Needed Training and Performance...

During human motor skill training and physical rehabilitation, there exists an inherent trade-off between task difficulty and user performance. Characterizing this trade-off is crucial for evaluating user performance, designing assist-as-needed (AAN) protocols, and assessing the efficacy of training protocols. In this study, we propose a novel human-in-the-loop (HiL) Pareto optimization approach to characterize the trade-off between the performance and the perceived challenge level of motor learning or rehabilitation tasks. We adapt Bayesian multi-criteria optimization to systematically and efficiently perform HiL Pareto characterizations. Our HiL optimization employs a hybrid model measuring performance with a quantitative metric, while the perceived challenge level is captured with a qualitative metric derived from preference-based user feedback. We demonstrate the utility of the framework through three use cases in the context of a manual skill training task with haptic feedback. First, we demonstrate how the characterized trade-off can be used to design a sample AAN training protocol and evaluate the group-level efficacy of the proposed AAN protocol relative to a baseline assistance protocol. Second, we demonstrate that individual-level comparisons of the trade-offs characterized before and after the training session enable fair evaluation of training progress under different assistance levels, providing insights even when users cannot perform the task without assistance. Third, we show that the characterized trade-offs also enable fair performance comparisons across users, since they capture each user's best possible performance at all feasible assistance levels.