Correcting pose estimates during tactile exploration of object shape: a neuro-robotic study

Robots are expected to operate autonomously in unconstrained, real-world environments. Therefore, they cannot rely on access to models of all objects in their environment, in order to parameterize object-directed actions. The robot must estimate the shape of objects in such environments, based on their perception. How to estimate an object's shape based on distal sensors, such as color- or depth cameras, has been extensively studied. Using haptic sensors for this purpose, however, has not been considered in a comparable depth. Humans, to the contrary, are able to improve object manipulation capabilities by using tactile stimuli, acquired from an active haptic exploration of an object. In this paper we introduce a neural-dynamic model which allows to build an object shape representation based on haptic exploration. Acquiring this representation during object manipulation requires the robot to autonomously detect and correct errors in the localization of tactile features with respect to the object. We have implemented an architecture for haptic exploration of an object's shape on a physical robotic hand in a simple exemplary scenario, in which the geometrical models of two different n-gons are learned from tactile data while rotating them with the robotic hand.