Leveraging symmetry detection to speed up haptic object exploration in robots

Most objects are symmetric. In fact, humans are very good at detecting symmetry, both by vision and by touch, and they use such information to facilitate the perception of other object properties, such as shape and size; overall, this contributes to human's ability to successfully manipulate objects in unstructured environments. Inspired by this human skill, in this paper we propose a haptic exploration procedure that enables a robot to detect object symmetry, and uses such information to estimate the shape of an object with higher accuracy and in less time. We achieve this by incorporating symmetries in a Gaussian Process model, and by introducing a novel strategy to detect the presence of such symmetry. We report results obtained with a Baxter robot equipped with a custom tactile sensor on the gripper: we show that when the robot explores objects with unknown symmetries the time required to estimate the object shape is reduced by up to 50% thanks to our method.