Implementing robotic grasping tasks using a biological approach

The capability of autonomously discovering relations between perceptual data and motor actions is crucial for the development of robust adaptive robotic systems intended to operate in a changing and unknown environment. In the case of robotic tactile perception, proper interaction between contact sensing and motor control is the basic step towards the execution of complex motor procedures such as grasping and manipulation. In this paper we propose an approach to the development of tactile-motor coordination in robotics, based on a neural model of the human tactile-motor system. The definition of such model is based on the features of biological systems as investigated by neuroscience. The autonomous development of tactile-motor coordination achieved through the implementation of the neural model is evaluated by experimental trials using a sensorised prosthetic hand and a robotic manipulator. The proposed neural network architecture linking changes in the sensed tactile pattern with the motor actions performed is described and experimental results are analysed and discussed.

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