This paper explores active sensing strategies that employ vision-based tactile sensors for robotic perception and classification of fabric textures. We formalize the active sampling problem in the context of tactile fabric recognition and provide an implementation of information-theoretic exploration strategies based on minimizing predictive entropy and variance of probabilistic models. Through ablation studies and human experiments, we investigate which components are crucial for quick and reliable texture recognition. Along with the active sampling strategies, we evaluate neural network architectures, representations of uncertainty, influence of data augmentation, and dataset variability. By evaluating our method on a previously published Active Clothing Perception Dataset and on a real robotic system, we establish that the choice of the active exploration strategy has only a minor influence on the recognition accuracy, whereas data augmentation and dropout rate play a significantly larger role. In a comparison study, while humans achieve 66.9% recognition accuracy, our best approach reaches 90.0% in under 5 touches, highlighting that vision-based tactile sensors are highly effective for fabric texture recognition.

翻译：本文探讨了利用基于视觉的触觉传感器进行机器人织物纹理感知与分类的主动感知策略。我们将主动采样问题形式化为触觉织物识别的框架，并实现了基于信息论的探索策略——通过最小化概率模型的预测熵与方差来引导采样。通过消融实验和人类对比实验，我们深入探究了实现快速可靠纹理识别的关键组件。在与主动采样策略的联合评估中，我们进一步分析了神经网络架构、不确定性表征方法、数据增强效果以及数据集多样性。在先前发布的主动服装感知数据集及真实机器人系统上的实验表明，主动探索策略的选择对识别准确率的影响较小，而数据增强与dropout率则发挥着显著更重要的作用。在人类对比实验中，人类受试者达到66.9%的识别准确率，而本方法在5次触觉采样内即实现90.0%的准确率，凸显出基于视觉的触觉传感器在织物纹理识别领域的高效性。