The body of work related to "sensor evolution" [1,2] analyses how sensors of a robot can adapt to "statistical structure of its current environment".
1. Olsson, L., Nehaniv, C.L., and Polani, D. From Unknown Sensors and Actuators to Actions Grounded in Sensorimotor Perceptions . Connection Science, 18(2). Special Issue on Developmental Robotics, Douglas Blank and Lisa Meeden, editors, 2006.
Abstract 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.
2. Olsson, L., Nehaniv, C.L., and Polani, D. Sensor Adaptation and Development in Robots by Entropy Maximization of Sensory Data . In CIRA, Espoo, Finland, 2005.
Abstract A method is presented for adapting the sensors of a robot to the statistical structure of its current environment. This enables the robot to compress incoming sensory information and to find informational relationships between sensors. The method is applied to creating sensoritopic maps of the informational relationships of the sensors of a developing robot, where the informational distance between sensors is computed using information theory and adaptive binning. The adaptive binning method constantly estimates the probability distribution of the latest inputs to maximize the entropy in each individual sensor, while conserving the correlations between different sensors...