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.
|Title of host publication||Unknown Host Publication|
|Number of pages||6|
|Publication status||Published - 22 Mar 2012|
|Event||Proc. of AAAI 2012 Spring Symposium on "Designing Intelligent Robots: Reintegrating AI" 2012 - Stanford|
Duration: 22 Mar 2012 → …
|Conference||Proc. of AAAI 2012 Spring Symposium on "Designing Intelligent Robots: Reintegrating AI" 2012|
|Period||22/03/12 → …|