# Study: Active sampling with vision-based tactile sensors for hardness classification

- Source: arXiv cs.RO
- Published: 2026-10-28T04:00:00.000Z
- RoboHot: https://robohot.news/en/items/ng33ur9kszbzpw0nfi7ss6hzc
- Original: https://arxiv.org/abs/2505.13231

## Summary

The paper investigates active sampling strategies based on vision-based tactile sensors (VBTS) for sample-efficient hardness classification, using information-theoretic measures of model uncertainty. The authors evaluated three probabilistic classifiers, two active sampling strategies, and a random sampling baseline on a hardness classification task using their own VBTS dataset collected by a robot and a previously published dataset, and also conducted a human study on hardness classification with the same objects. Results show that the active sampling methods using variance and entropy as uncertainty measures performed best, reaching 90% accuracy, compared with human accuracy of 48%.