Abstract
Tactile gesture recognition systems play a crucial role in Human-Robot Interaction (HRI) by enabling intuitive communication between humans and robots. The literature mainly addresses this problem by applying machine learning techniques to classify sequences of tactile images encoding the pressure distribution generated when executing the gestures. However, some gestures can be hard to differentiate based on the information provided by tactile images alone. In this paper, we present a simple yet effective way to improve the accuracy of a gesture recognition classifier. Our approach focuses solely on processing the tactile images used as input by the classifier. In particular, we propose to explicitly highlight the dynamics of the contact in the tactile image by computing the dense optical flow. This additional information makes it easier to distinguish between gestures that produce similar tactile images but exhibit different contact dynamics. We validate the proposed approach in a tactile gesture recognition task, showing that a classifier trained on tactile images augmented with optical flow information achieved a 9% improvement in gesture classification accuracy compared to one trained on standard tactile images.
Abstract (translated)
触觉手势识别系统在人机交互（HRI）中扮演着至关重要的角色，通过实现人类与机器人之间的直观交流来发挥作用。现有文献主要通过应用机器学习技术来分类编码手势执行过程中产生的压力分布的触觉图像序列，以此解决这一问题。然而，仅凭触觉图像是难以区分某些手势的。在这篇论文中，我们提出了一种简单而有效的方法来提高手势识别分类器的准确性。我们的方法专注于处理分类器使用的输入触觉图像。具体来说，我们建议通过计算密集光流来明确突出触觉图像中的接触动态，这将有助于区分那些虽然生成相似触觉图但表现出不同接触动态的手势。我们在一个触觉手势识别任务中验证了所提出的方法，并展示了基于光学流量信息增强的触觉图像训练得到的分类器在手势分类准确性方面相较于仅使用标准触觉图像训练提高了9%。
URL
https://arxiv.org/abs/2508.04338
