Tactile image based contact shape recognition using neural network

This paper proposes a novel algorithm for recognizing the shape of object which in contact with a robotic finger through the tactile pressure sensing. The developed algorithm is capable of distinguishing the contact shapes between a set of low-resolution pressure map. Within this algorithm, a novel feature extraction technique is developed which transforms a pressure map into a 512-feature vector. The extracted feature of the pressure map is invariant to scale, positioning and partial occlusion, and is independent of the sensor´s resolution or image size. To recognize different contact shape from a pressure map, a neural network classifier is developed and uses the feature vector as inputs. It has proven from tests of using four different contact shapes that, the trained neural network can achieve a high success rate of over 90%. Contact sensory information plays a crucial role in robotic hand gestures. The algorithm introduced in this paper has the potential to provide valuable feedback information to automate and improve robotic hand grasping and manipulation.