Exploration and exploitation of sensorimotor contingencies for a cognitive embodied agent

The modelling of cognition is playing a major role in robotics. Indeed, robots need to learn, adapt and plan their actions in order to interact with their environment. To do so, approaches like embodiment and enactivism propose to ground sensorimotor experience in the robot's body to shape the development of cognition. In this work, we focus on the role of memory during learning in a closed loop. As sensorimotor contingencies, we consider a robot arm that moves a baby mobile toy to get visual reward. First, the robot explores the continuous sensorimotor space by associating visual stimuli to motor actions through motor babbling. After exploration, the robot uses the experience from its memory and exploits it, thus optimizing its motion to perceive more visual stimuli. The proposed approach uses Dynamic Field Theory and is integrated in the GummiArm, a 3D printed humanoid robot arm. The results indicate a higher visual neural activation after motion learning and show the benefits of an embodied babbling strategy.