Abstract

The fragmentation of research in AI and robotics has created a vast repertoire of skills a robot could be equipped with but that hardly integrate to form a complex action. We propose a novel evolutionary algorithm that aims at autonomously integrate, adapt and create new actions by re-using skills that are either externally provided or previously generated. Complex actions are created by instantiating a Finite State Automaton and new skills are created using fully recurrent neural networks.We validated our approach in two scenarios, i.e. exploration and moving to pre-grasp positions. Our experiments show that complex actions can be created by composing independently developed skills. The results have been applied and tested with a real robot in a variety of scenarios.