Personalized Lower-limb Exoskeleton Assistance via Preference-based Bayesian Optimization
2026-08-10 • Robotics
Robotics
AI summaryⓘ
The authors address the challenge of customizing exoskeleton control settings to match each person's walking preferences without needing long and tiring adjustment sessions. They introduce a method called preference-based Bayesian optimization (PbBO) that quickly finds the best settings by learning from fewer user feedbacks. Testing showed that their method can effectively reduce the effort and strain on the user during walking, as seen by lower heart rate, muscle activity, and energy use. Their approach also works in real time for different walking tasks. Overall, the authors demonstrate a faster and more comfortable way to personalize exoskeleton assistance.
Exoskeleton roboticsUser preference-based optimizationBayesian optimizationControl parametersMetabolic rateMuscle activationTorque trackingPersonalized assistance
Authors
Xiao-Yin Liu, Guotao Li, Weiqun Wang, Zeng-Guang Hou
Abstract
A significant challenge in exoskeleton robotics is the need to dynamically adapt control profiles to individual motion preferences, thereby ensuring both efficient and comfortable assistance. Currently, since user experience can serve as a comprehensive metric for evaluating the effectiveness of assistance, user preference-based optimization methods have been widely studied for parameter tuning. However, the existing methods rely heavily on extensive human-robot online interactions and suffer from slow optimization speed, which not only induces user fatigue but also compromises optimization effectiveness. Therefore, this paper aims to explore an efficient preference-based optimization framework for personalized exoskeleton assistance that can learn optimal parameters with minimal interaction. We propose a preference-based Bayesian optimization (PbBO) approach that can improve sample efficiency by leveraging knowledge about the sampling distribution of candidate sets. For optimizing six control parameters, PbBO can converge to user-preferred parameters with 90.7% validation accuracy via 20 iterations. Moreover, the hierarchical controller is designed to generate personalized torque for different tasks and achieve interaction torque tracking in real time. The results of treadmill and outdoor experiments demonstrate that the optimized parameters can reduce metabolic rate by 14.5%-15.4%, heart rate by 6.3%-7.6%, and muscle activation by 6.7%-31.5% compared to unassisted walking.