发表机构
Institute of Automation, Chinese Academy of Sciences; University of Chinese Academy of Sciences; Macau University of Science and Technology（中国科学院自动化研究所; 中国科学院大学; 澳门科技大学）
机构由 AI 辅助整理，请以论文原文为准。AI 中文总结
本文提出偏好贝叶斯优化（PbBO）方法，结合分层控制器，实现个性化下肢外骨骼助力，经20次迭代以90.7%准确率收敛最优参数，实验显示可显著降低用户代谢率、心率及肌肉激活度。
AI 中文摘要
外骨骼机器人领域的一大挑战是需要动态调整控制参数以适配个体运动偏好，从而确保助力既高效又舒适。当前，用户体验可作为评估助力效果的综合指标，基于用户偏好的优化方法已被广泛用于参数调优。然而，现有方法严重依赖大量人机在线交互，优化速度缓慢，不仅会导致用户疲劳，还会降低优化效果。因此，本文旨在探索一种用于个性化外骨骼助力的高效偏好优化框架，该框架可通过最少的交互学习最优参数。我们提出了一种偏好贝叶斯优化（PbBO）方法，该方法可利用候选集的采样分布知识提高样本效率。针对六个控制参数的优化，PbBO 可在 20 次迭代后以 90.7% 的验证准确率收敛到用户偏好的参数。此外，我们设计了分层控制器，用于为不同任务生成个性化扭矩并实时实现交互扭矩跟踪。跑步机和户外实验结果表明，与无助力行走相比，优化后的参数可使代谢率降低 14.5%-15.4%，心率降低 6.3%-7.6%，肌肉激活度降低 6.7%-31.5%。
英文摘要
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.
Comments20 pages, 15 figures, https://youtu.be/8X1SFqUU4G4
