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Touch perception is an important sense to model in humanoid robots to interact physically and socially with humans. We present a neural controller that can adapt the compliance of the robot arm in four directions using as input the tactile information from an artificial skin and as output the estimated torque for admittance control-loop reference. This adaption is done in a self-organized fashion...
The learning of sensorimotor primitives in an open-ended manner is important to achieve all the possible tasks a robot can do, even those never experienced before. In this short paper, we propose a neural architecture called Dynamic Sensorimotor Model (DSM) (1) that learn co-variation rules between sensors and motors for sensorimotor prediction, (2) use these predictions for action planning. This...
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