LAUSR

Recognizing objects by touch is a very useful skill for robots to be employed in both structured and unstructured environments. While in some applications it is useful to recognize an object from a single touch, in other scenarios specific robot movements can be used to obtain more information about the object, making recognition easier. In this letter, we show how this can be obtained through the combination of: (i) a recently developed tactile sensor that measures both normal and shear forces on multiple contact points, and (ii) an exploratory procedure that involves dynamic shaking of the gripped object. We compare the recognition accuracy in three conditions: static (i.e. single touch), short dynamic (i.e. using a small fraction of the exploratory procedure), and dynamic (i.e. using the entire exploratory procedure). We report experiments with six different machine learning techniques, and several combinations of tactile features, to recognize ten objects. Overall, our results demonstrate that: (i) the sensor we use is well suited for recognizing grasped objects with high accuracy, and (ii) the dynamic exploratory procedure provides a 38% improvement over single touch recognition. We make our data and code publicly available, to encourage reproduction of our results.