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In a developmental framework, autonomous robots need to explore the world andlearn how to interact with it. Without an a priori model of the system, thisopens the challenging problem of having robots master their interface with theworld: how to perceive their environment using their sensors, and how to act init using their motors. The sensorimotor approach of perception claims that anaive agent can learn to master this interface by capturing regularities in theway its actions transform its sensory inputs. In this paper, we apply such anapproach to the discovery and mastery of the visual field associated with avisual sensor. A computational model is formalized and applied to a simulatedsystem to illustrate the approach.

Autonomous Grounding of Visual Field Experience through Sensorimotor Prediction