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The young infant explores its body, its sensorimotor system, and theimmediately accessible parts of its environment, over the course of a fewmonths creating a model of peripersonal space useful for reaching and graspingobjects around it. Drawing on constraints from the empirical literature oninfant behavior, we present a preliminary computational model of this learningprocess, implemented and evaluated on a physical robot. The learning agentexplores the relationship between the configuration space of the arm, sensingjoint angles through proprioception, and its visual perceptions of the hand andgrippers. The resulting knowledge is represented as the peripersonal space(PPS) graph, where nodes represent states of the arm, edges represent safemovements, and paths represent safe trajectories from one pose to another. Inour model, the learning process is driven by intrinsic motivation. Whenrepeatedly performing an action, the agent learns the typical result, but alsodetects unusual outcomes, and is motivated to learn how to make those unusualresults reliable. Arm motions typically leave the static background unchanged,but occasionally bump an object, changing its static position. The reach actionis learned as a reliable way to bump and move an object in the environment.Similarly, once a reliable reach action is learned, it typically makes aquasi-static change in the environment, moving an object from one staticposition to another. The unusual outcome is that the object is accidentallygrasped (thanks to the innate Palmar reflex), and thereafter moves dynamicallywith the hand. Learning to make grasps reliable is more complex than forreaches, but we demonstrate significant progress. Our current results are stepstoward autonomous sensorimotor learning of motion, reaching, and grasping inperipersonal space, based on unguided exploration and intrinsic motivation.

Learning and Acting in Peripersonal Space: Moving, Reaching, and Grasping