Inferring Probabilistic Automata from Sensor Data for Robot Navigation Anke Rieger Inferring Probabilistic Automata from Sensor Data for Robot Navigation Anke Rieger

We address the problem of guiding a robot in such a way, that it can decide, based on perceived sensor data, which future actions to choose, in order to reach a goal. In order to realize this guidance, the robot has access to a (probabilistic) automaton (PA), whose nal states represent concepts, which have to be recognized in order to verify, that a goal has been achieved. The contribution of this work is to learn these PA's from classi ed sensor data of robot traces through known environments. Within this framework, we account for the uncertainties arising from ambiguous perceptions. We introduce a knowledge structure, called pre x tree, in which the sample data, represented as cases, is organized. The pre x tree is used to derive and estimate the parameters of deterministic, as well as probabilistic automata models, which re ect the inherent knowledge, implicit in the data, and which are used for recognition in a restricted rst-order logic framework. ( This paper is also published in M. Kaiser (ed.), Proceedings of the Third European Workshop on Learning Robots, 1995.)