Using Sensorimotor Contingencies for Prediction and Action Planning

Abstract

Sensorimotor contingency theory holds that the law-like relations between actions and contingent changes in the sensory signals constitute the basis for sensory experience and awareness in humans. These Sensory-Motor Contingencies (SMCs) are not only passively observed and recorded by the agent, but are actively exercised and used to control behavior. We have previously introduced a computational model of SMCs for robot control that employs a set of Markov models for the conditional probabilities of making sensory observations given an action. In this article we extend this model by showing how prediction and evaluation of future sensorimotor events can be achieved. We investigate this prediction and planning method in a scenario where the robot’s actions do not take immediately effect, so that it has to plan ahead. Exploiting an action selection method that takes into account previous experiences, the robot learns to move in an energy-efficient, naturalistic manner and to avoid known obstacles. We also make a first step towards analyzing the robot’s behavior in a dynamically changing environment.