用少次交互优化外骨骼参数，让穿戴更省力舒适。
Personalized Lower-limb Exoskeleton Assistance via Preference-based Bayesian Optimization
- 基于用户偏好构建贝叶斯优化框架，提升参数搜索效率。
- 20次迭代后准确率达90.7%，代谢率降低14.5%-15.4%。
- 适合追求个性化、低疲劳外骨骼控制的研究与应用。
外骨骼机器人面临动态适配个体运动偏好的挑战，以实现高效舒适的辅助。当前方法依赖大量人机在线交互，优化速度慢，易导致用户疲劳且影响效果。本文提出一种基于偏好的贝叶斯优化（PbBO）框架，通过利用候选集采样分布知识提升样本效率。针对六个控制参数，仅需20次迭代即可达到90.7%的验证准确率。同时设计分层控制器，实现实时交互扭矩跟踪并生成任务自适应扭矩。跑步机与户外实验表明，优化参数相比无辅助行走可降低代谢率14.5%-15.4%、心率6.3%-7.6%、肌肉激活6.7%-31.5%。
原文摘要 · Abstract (English)
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.
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