Abstract

Hardness is a key tactile property perceived by humans and robots. In this work, we investigate information- theoretic active sampling for efficient hardness classification using vision-based tactile sensors. We assess three probabilistic classifiers and two uncertainty-based sampling strategies on a robotic setup and a human-collected dataset. Results show that uncertainty-driven sampling outperforms random sampling in accuracy and stability. While human participants achieve 48.00% accuracy, our best method reaches 88.78% on the same objects, highlighting the effectiveness of vision-based tactile sensors for hardness classification.