Autonomous sensory-motor systems, situated in dynamic environments, must continuously answer the ultimate question: how to control motor commands knowing sensory inputs? Solving this question is a very complex problem, because a huge flow of information must be treated under several restrictions: real-time constraints, bounded memory space, and limited processing power. One additional and major challenge is to deal with incomplete and imprecise information, usually occurring in dynamic environments. In this thesis, we address the problem of controlling autonomous sensory-motor systems and propose a succession of cumulative hypotheses and simplifications. They are defined within a precise and strict mathematical framework, called Bayesian programming, an extension of Bayesian networks. This succession consists of five stages: Utilisation of internal states; First-order Markov assumption, stationarity and Bayesian filters; Exploiting partial independence; Addition of behaviour selection mechanism; and addition of Attention focusing. The validity of these hypotheses can be discussed and challenged. However, the corresponding inferences and resulting decisions are derived mathematically. Each description of a stage is followed by its analysis according to memory requirement, processing complexity, and difficulty of modelling. Further discussions regarding robot programming and cognitive modelling points of view are also presented. Finally, we describe an implementation on a mobile robot. The results demonstrate that the proposed framework is adequate for practical purposes.
Keywords: Autonomous Robotics – Probabilistic Model – Action Selection – Selective Perception