  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.
