Hardness classification using cost-effective off-the-shelf tactile sensors inspired by mechanoreceptors

Perception is essential for robotic systems, enabling effective interaction with their surroundings through actions such as grasping and touching. Traditionally, this has relied on integrating various sensor systems, including tactile sensors, cameras, and acoustic sensors. This study leverages commercially available tactile sensors for hardness classification, drawing inspiration from the functionality of human mechanoreceptors in recognizing complex object properties during grasping tasks. Unlike previous research using customized sensors, this study focuses on cost-effective, easy-to-install, and readily deployable sensors. The approach employs a qualitative method, using Shore hardness taxonomy to select objects and evaluate the performance of commercial off-the-shelf (COTS) sensors. The analysis includes data from both individual sensors and their combinations analysed using multiple machine learning approaches, and accuracy as the primary evaluation metric was considered. The findings illustrate that increasing the number of classification classes impacts accuracy, achieving 92% in binary classification, 82% in ternary, and 80% in quaternary scenarios. Notably, the performance of commercially available tactile sensors is comparable to those reported in the literature, which range from 50% to 98% accuracy, achieving 92% accuracy with a limited data set. These results highlight the capability of COTS tactile sensors in hardness classification giving accuracy levels of 92%, while being cost-effective and easier to deploy than customized tactile sensors.