Learning emergent tasks for an autonomous mobile robot

We present an implementation of a reinforcement learning algorithm through the use of a special neural network topology, the AHC (adaptive heuristic critic). The AHC is used as a fusion supervisor of primitive behaviors in order to execute more complex robot behaviors, for example go to goal, surveillance or follow a path. The fusion supervisor is part of an architecture for the execution of mobile robot tasks which are composed of several primitive behaviors which act in a simultaneous or concurrent fashion. The architecture allows for learning to take place at the execution level, it incorporates the experience gained in executing primitive behaviors as well as the overall task. The implementation of this autonomous learning approach has been tested within OPMOR, a simulation environment for mobile robots and with our mobile platform, the UPM Robuter. Both, simulated and actual results are presented. The performance of the AHC neural network is adequate. Portions of this work has been implemented within the EEC ESPRIT 2483 PANORAMA Project