Abstract
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.
Original language | English |
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Title of host publication | Unknown Host Publication |
Publisher | IEEE |
Number of pages | 8 |
Publication status | Published (in print/issue) - 10 Jun 2012 |
Event | 2012 IEEE Congress on Evolutionary Computation - Brisbane, Australia Duration: 10 Jun 2012 → … |
Conference
Conference | 2012 IEEE Congress on Evolutionary Computation |
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Period | 10/06/12 → … |