发表机构
Korea University; WIRobotics（高丽大学; WIRobotics）
机构由 AI 辅助整理，请以论文原文为准。AI 中文总结
提出上下文连续偏好学习（CCPL），利用高斯过程在邻近操作条件间共享偏好数据，在有限反馈下提升外骨骼个性化效率，模拟和人体数据验证了其有效性。
AI 中文摘要
在不同操作条件下个性化外骨骼辅助受到收集用户反馈所需时间和体力的限制。我们考察了用户的偏好景观是否随操作条件平滑变化，以及这种连续性是否支持从有限反馈中学习。我们提出了上下文连续偏好学习（CCPL），一种高斯过程偏好模型，它在邻近上下文间共享观测数据，同时保留特定上下文的效用估计。我们通过模拟和对九名健康成年人的踝关节和肘关节外骨骼偏好数据的回顾性分析评估了CCPL。在模拟中，当偏好平滑变化时，CCPL相对于独立学习改善了重建和基于偏好的贝叶斯优化，但在连续性较弱时表现出负迁移。在两项人体研究中，为每个参与者和上下文分别估计的全数据参考景观在邻近操作条件之间往往更相似。在每上下文五次暴露的情况下，相对于独立学习，CCPL将踝关节辅助的平均重建相关性与这些参考的相关性从0.644提高到0.720，肘关节辅助从0.476提高到0.526。五次暴露的预算比达到这些相关性所需的估计独立学习预算低约37%（踝关节）和17%（肘关节）。CCPL还改善了相对于独立学习的留出响应预测，而相对于合并学习的收益则有所不同。这些发现支持上下文连续性作为在有限反馈下共享偏好观测的基础，尽管对在线个性化在人类中的益处仍有待证实。
英文摘要
Personalizing exoskeleton assistance across operating conditions is constrained by the time and physical effort required to collect user feedback. We examined whether a user's preference landscape varies smoothly across operating conditions and when this continuity supports learning from limited feedback. We propose Context-Continuous Preference Learning (CCPL), a Gaussian-process preference model that shares observations across nearby contexts while retaining context-specific utility estimates. We evaluated CCPL through simulations and retrospective analyses of ankle and elbow exoskeleton preference data from nine healthy adults. In simulations, CCPL improved reconstruction and preference-based Bayesian optimization relative to independent learning when preferences varied smoothly, but showed negative transfer when continuity was weak. In both human studies, full-data reference landscapes estimated separately for each participant and context tended to be more similar between nearby operating conditions. With five exposures per context, CCPL increased mean reconstruction correlation with these references from 0.644 to 0.720 for ankle assistance and from 0.476 to 0.526 for elbow assistance relative to independent learning. The five-exposure budget was approximately 37% lower for ankle and 17% lower for elbow than the estimated independent-learning budgets needed to match these correlations. CCPL also improved held-out response prediction relative to independent learning, while benefits over pooled learning varied. These findings support context continuity as a basis for sharing preference observations under limited feedback, although benefits for online personalization in humans remain to be established.
Comments23 pages, 11 figures, including supplementary materials
