Haptic object identification for advanced manipulation skills

In order to identify the characteristics of unknown objects, humans-in contrast to robotic systems-are experts in exploiting their sensory and motoric abilities to refine visual information via haptic perception. While recent research has focused on either estimating the geometry or material properties, this work strives to combine these aspects by outlining a probabilistic framework that efficiently refines initial knowledge from visual sensors by generating a belief state over the object shape while simultaneously learn material parameters. Specifically, we present a grid-based and a shape-based exploration strategy, that both apply the concepts of Bayesian-Filter theory in order to decrease the uncertainty. Furthermore, the presented framework is able to learn about the geometry as well as to distinguish areas of different material types by applying unsupervised machine learning methods. The experimental results from a virtual exploration task highlight the potential of the presented methods towards enabling robots to autonomously explore unknown objects, yielding information about shape and structure of the underlying object and thus, opening doors to robotic applications where environmental knowledge is limited.