Real-Time Hardness Prediction Using COTS Tactile Sensors in Robotic Grippers

In material classification tasks, custom sensors have traditionally been employed to achieve accuracy scores. While numerous studies have reported high accuracy rates, there has been limited discussion on real-time predictions or real-world applications in most research papers. The real-time prediction of object material properties is crucial for enhancing the tactile sensing capabilities of robotics in industrial settings. This study proposes the use of Commercial Off-The-Shelf (COTS) tactile sensors for hardness classification, utilizing small datasets for model training and real-time prediction. Testing involves evaluating the ability of robotic grippers to accurately predict the hardness of new, unknown objects, categorizing them into two classes (soft, hard) or three classes (hard, soft, flexible). Results obtained from a multiple-algorithm approach reveal an 80% accuracy rate for binary classification, with real-time tests demonstrating two out of three correct predictions for most sensors. For ternary classification, the accuracy rate is around 70%, with two out of three correct predictions from at least one sensor. These findings highlight the capability of COTS sensors to perform real-time hardness classification effectively. This also highlights that COTS sensors have capabilities and flexibility based on their dimensional architecture that can be used in many different robotics applications without investing time in the development of a specific use-case sensor for classification tasks within robotic tactile sensing.