Robotic sensorimotor learning in continuous domains

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Abstract
The authors propose that some aspects of task-based learning in robotics can be approached using nativist and constructionist views on human sensorimotor development as a metaphor. They use findings in developmental psychology and neurophysiology, as well as machine perception, to guide the overall design of robotic system that attempts to learn sensorimotor binding rules for simple actions. Visually driven grasping was chosen as the experimental task. The learning was empirical in nature, and was done by having the robot observe repeated interactions with the task environment. The technique of nonparametric projection pursuit regression was used to accomplish reinforcement data sets that capture task invariants. The learning process generally implied failures along the way. Therefore, the mechanics of the untrained robotic system must be able to tolerate mistakes during learning and not be damaged. This problem was addressed by the use of an instrumented compliant robot wrist that controlled impact forces.

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