We propose an evolutionary algorithm to au- tonomously improve the performances of a robotics skill. The algorithm extends a previously proposed graphical evolutionary skills building approach to allow a robot to autonomously collect use cases where a skill fails and use them to improve the skill. Here we define a computational graph as a generic model to hierarchically represent skills and to modify them. The computational graph makes use of embedded neural networks to create generic skills. We tested our proposed algorithm on a real robot implementing a “move to reach” action. Four experiments show the evolution of the computational graph as it is adapted to solve increasingly complex problems.
|Title of host publication||Unknown Host Publication|
|Number of pages||8|
|Publication status||Published - 10 Jun 2012|
|Event||2012 IEEE Congress on Evolutionary Computation - Brisbane, Australia|
Duration: 10 Jun 2012 → …
|Conference||2012 IEEE Congress on Evolutionary Computation|
|Period||10/06/12 → …|