Combining sensory modalities and exploratory procedures to improve haptic object recognition in robotics

In this paper we tackle the problem of object recognition using haptic feedback from a robot holding and manipulating different objects. One of the main challenges in this setting is to understand the role of different sensory modalities (namely proprioception, object weight from F/T sensors and touch) and how to combine them to correctly discriminate different objects. We investigated these aspects by considering multiple sensory channels and different exploratory strategies to gather meaningful information regarding the object's physical properties. We propose a novel strategy to train a learning machine able to efficiently combine sensory modalities by first learning individual object features and then combine them in a single classifier. To evaluate our approach and compare it with previous methods we collected a dataset for haptic object recognition, comprising 11 objects that were held in the hands of the iCub robot while performing different exploration strategies. Results show that our strategy consistently outperforms previous approaches [17].