A robot that autonomously improves skills by evolving computational graphs

We propose an evolutionary algorithm to autonomously 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.