Article

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

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%.

Part of the event: Active Sampling for Hardness Classification with Vision-Based Tactile Sensors

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