解决远程康复中延迟导致的僵硬度误判问题
Delay-Compensated Stiffness Estimation for Robot-Mediated Dyadic Interaction
- 基于准静态平衡推导代数估计算法，对齐专家输入与新手响应时间
- 在多延迟条件下仍保持高精度，误差显著低于传统方法
- 适用于远程康复等需精准触觉感知的医疗场景
机器人中介的人-人（双人）互动可使治疗师远程提供物理治疗，但网络引起的触觉延迟使得患者僵硬度的准确感知仍具挑战性。传统僵硬度估计方法忽略延迟，导致力信号与位置信号在时间上错位，随延迟增加误差显著增大。为此，我们提出一种鲁棒的延迟补偿僵硬度估计框架，通过基于准静态平衡推导的代数估计算法，显式实现专家输入与新手响应的时间对齐。随后引入归一化加权最小二乘（NWLS）实现，以稳健滤除代数推导带来的动态偏差。在商用康复机器人（H-MAN）平台上进行的实验表明，所提方法显著优于标准估计器，在多种引入延迟条件下均保持一致的跟踪精度。研究结果为远程双人互动中实现高保真触觉感知提供了可行方案，有望支持跨网络环境下的可靠僵硬度评估。
原文摘要 · Abstract (English)
Robot-mediated human-human (dyadic) interactions enable therapists to provide physical therapy remotely, yet an accurate perception of patient stiffness remains challenging due to network-induced haptic delays. Conventional stiffness estimation methods, which neglect delay, suffer from temporal misalignment between force and position signals, leading to significant estimation errors as delays increase. To address this, we propose a robust, delay-compensated stiffness estimation framework by deriving an algebraic estimator based on quasi-static equilibrium that explicitly accounts for temporally aligning the expert's input with the novice's response. A Normalised Weighted Least Squares (NWLS) implementation is then introduced to robustly filter dynamic bias resulting from the algebraic derivation. Experiments using commercial rehabilitation robots (H-MAN) as the platform demonstrate that the proposed method significantly outperforms the standard estimator, maintaining consistent tracking accuracy under multiple introduced delays. These findings offer a promising solution for achieving high-fidelity haptic perception in remote dyadic interaction, potentially facilitating reliable stiffness assessment in therapeutic settings across networks.
Thank you to arXiv for use of its open access interoperability. PaperDance 不是 arXiv 官方产品；中文卡片由大模型生成，请以原文为准。