What Matters for Active Texture Recognition With Vision-Based Tactile Sensors
This work addresses fabric texture recognition for robotics, but it is incremental as it builds on existing datasets and methods.
The paper tackled the problem of active texture recognition using vision-based tactile sensors for robotics, finding that active exploration strategies had minor influence while data augmentation and dropout rate were more critical, achieving 90.0% accuracy in under 5 touches compared to 66.9% for humans.
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.