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Human-in-the-Loop Pareto Optimization: Trade-Off Characterization for Assist-as-Needed Training and Performance
IEEE Transactions on Haptics
|April 1, 2026
Summary
This study introduces a human-in-the-loop Pareto optimization approach to balance task difficulty and performance in motor rehabilitation. This method enhances training protocols and user performance evaluation.
Area of Science:
- Robotics
- Human-Computer Interaction
- Rehabilitation Engineering
Background:
- A key challenge in motor skill training and rehabilitation is the trade-off between task difficulty and user performance.
- Accurate characterization of this trade-off is essential for effective training protocol design, user performance evaluation, and the development of adaptive assistance strategies.
Purpose of the Study:
- To propose and validate a novel human-in-the-loop (HiL) Pareto optimization framework for characterizing the performance-challenge trade-off in motor learning and rehabilitation tasks.
- To demonstrate the framework's utility in designing adaptive assistance protocols and enabling fair performance evaluations at both group and individual levels.
Main Methods:
- Adaptation of Bayesian multi-criteria optimization for efficient HiL Pareto characterization.
- Hybrid model integrating quantitative performance metrics with qualitative user feedback for perceived challenge assessment.
- Application within a manual skill training task incorporating haptic feedback.
Main Results:
- Demonstrated the framework's ability to design an assist-as needed (AAN) training protocol and evaluate its efficacy against a baseline.
- Showcased individual-level progress assessment through pre- and post-training trade-off comparisons, offering insights even for users requiring assistance.
- Validated the framework for fair cross-user performance comparisons by capturing optimal performance across assistance levels.
Conclusions:
- The proposed HiL Pareto optimization framework provides a robust method for characterizing the performance-challenge trade-off in motor rehabilitation.
- This approach facilitates the design of personalized AAN protocols, enables nuanced evaluation of training effectiveness, and allows for equitable comparisons across users.
- The framework offers significant potential for advancing human-robot interaction in therapeutic and skill-acquisition contexts.
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