This article describes a developmental system based on information theory implemented on a real robot that learns a model of its own sensory and actuator apparatus. There is no innate knowledge regarding the modalities or representation of the sensory input and the actuators, and the system relies on generic properties of the robot’s world such as piecewise smooth effects of movement on sensory changes. The robot develops the model of its sensorimotor system by first performing random movements to create an informational map of the sensors. Using this map the robot then learns what effects the different possible actions have on the sensors. After this developmental process the robot can perform basic visually guided movement.

Abstract — This paper describes a developmental system implemented on a real robot that learns a model of its own sensory and actuator apparatuses. There is no innate knowledge regarding the modality or representation of the sensoric input and the actuators, and the system relies on generic properties of the robot’s world such as piecewise smooth effects of movement on sensory changes. The robot develops the model of its sensorimotor system by first performing random movements to create an informational map of the sensors. Using this map the robot then learns what effects the different possible actions have on the sensors. After this developmental process the robot can perform simple motion tracking. Index Terms — developmental robotics, body babbling, emergence of structure I.

In embodied artificial intelligence is it of interest to study the informational relationships between the agent, its actions, and the environment. This paper presents a number of statistical measures to compute the informational distance between sensors including the information metric, correlation coefficient, Hellinger distance, Kullback-Leibler, and Jensen-Shannon divergence. The methods are compared using the sensory reconstruction method to find spatial positions of visual sensors of different modalities in a sensor integration task. The results show how the information metric can find relations not found by the other measures.