用视觉学习软手指外骨骼的接触力，实现无感实时反馈控制。
Learning User Interaction Forces using Vision for a Soft Finger Exosuit
- 基于低分辨率灰度图像，通过学习估计多点接触力。
- 在未见过的形状和动作下仍能准确估计，抗干扰能力强。
- 可替代传统传感器，适合智能外骨骼闭环控制场景。
可穿戴辅助设备正趋向柔性化，其与人体组织的交互建模对动态辅助传递至关重要。然而，其非线性和柔顺特性使得物理建模和嵌入式传感均具挑战。本文提出一种基于图像的、学习驱动的方法，用于估算手指外骨骼系统的分布接触力。利用SoRoSim工具箱生成多样化的外骨骼几何结构与驱动场景数据集进行训练。该方法仅需低分辨率灰度图像，即可准确估计多个接触点的交互力，在未见形状与驱动水平上具有泛化能力，并对视觉噪声和对比度变化保持鲁棒性。将模型集成至反馈控制器后，验证了视觉估测器可作为闭环控制中的力传感器替代方案。该方法为外骨骼提供了非侵入式的实时力估计算法。
原文摘要 · Abstract (English)
Wearable assistive devices are increasingly becoming softer. Modelling their interface with human tissue is necessary to capture transmission of dynamic assistance. However, their nonlinear and compliant nature makes both physical modeling and embedded sensing challenging. In this paper, we develop a image-based, learning-based framework to estimate distributed contact forces for a finger-exosuit system. We used the SoRoSim toolbox to generate a diverse dataset of exosuit geometries and actuation scenarios for training. The method accurately estimated interaction forces across multiple contact locations from low-resolution grayscale images, was able to generalize to unseen shapes and actuation levels, and remained robust under visual noise and contrast variations. We integrated the model into a feedback controller, and found that the vision-based estimator functions as a surrogate force sensor for closed-loop control. This approach could be used as a non-intrusive alternative for real-time force estimation for exosuits.
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