Developmental robotics seeks to build robots that learn to interact with the environment largely autonomously. These robots can calibrate their sensorimotor competencies on their own, much like developing children. In this paper, we build a developmental model of image stabilization based on the active efficient coding (AEC) framework and apply the model to a real robotic platform. In the visual system of primates, the optokinetic response (OKR) and the vestibulo-ocular reflex (VOR) cooperate to ensure image stabilization during relative motion between the observer and the environment. Inspired by these biological findings, our model integrates visual, inertial and motor encoder sensory cues. The sensory processing and the motor policy co-develop. The visual processing is based on a sparse coding algorithm. Motor behavior is learned using reinforcement learning. Our results show that the stabilization performance is improved by integrating visual and inertial inputs. Importantly, the weighting between the two inputs is learned automatically as the robot interacts with the environment.

Developmental robots require cognitive structures that can learn perception-action cycles via interactions with the environment. Here, we extend the efficient coding hypothesis, which has been used to model the development of sensory processing in isolation, to model the development of the perception-action cycle. Our extension combines sparse coding and reinforcement learning so that sensory processing and behavior co-develop to optimize a shared intrinsic motivational signal: the fidelity of the neural encoding of the sensory input under resource constraints. Applying this framework to a model of a robot actively observing a time-varying environment leads to the simultaneous development of visual smooth pursuit behavior and model neurons similar to cortical neurons selective to visual motion. We suggest that this general principle may form the basis for a unified and integrated approach to learning many other perception/action loops.