In this paper, we present an approach for haptic object recognition and its evaluation on multi-fingered robot hands. The recognition approach is based on extracting key features of tactile and kinesthetic data from multiple palpations using a clustering algorithm. A multi-sensory object representation is built by fusion of tactile and kinesthetic features. We evaluated our approach on three robot hands and compared the recognition performance using object sets consisting of daily household objects. Experimental results using the five-fingered hand of the humanoid robot ARMAR, the three-fingered Schunk Dexterous Hand 2 and a parallel Gripper are performed. The results show that the proposed approach generalizes to different robot hands. Keywords: Object Recognition, Bag of Keypoints, Robot Hands, Tactile Sensing.
['Stefan Escaida Navarro', 'Nicolas Gorges', 'Heinz Wörn', 'Julian Schill', 'Tamim Asfour', 'Rüdiger Dillmann']
Haptic object recognition for multi-fingered robot hands