Learning to Guide a Robot via Perceptions

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 classiied sensor data of robot traces through known environments. Within this framework, we account for the uncertainties arising from ambiguous perceptions.