Autonomous learning of tactile-motor coordination in robotics

The development of autonomous systems capable of executing intelligent exploratory procedures requires the understanding of the interactions between touch and motor control modalities. In robotic tactile perception this is the basic step toward the execution of highly sophisticated motor procedures such as grasping and manipulation. In this paper, the problem of autonomous learning of tactile-motor coordination is investigated in the case of a robotic system composed of a multi-functional tactile probe mounted on a robotic manipulator. A neural network architecture linking changes in the sensed tactile pattern with the motor action performed is described, and experimental results are analyzed. The generation of motor control procedures for actively estimating surface curvature is considered as an example of application of the proposed approach