Learning multisensory cue integration on mobile robots

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.