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http://hdl.handle.net/10071/14922
acessibilidade
Title: Evolutionary online behaviour learning and adaptation in real robots
Authors: Silva, F.
Correia, L.
Christensen, A. L.
Keywords: Online evolution
Learning
Fault tolerance
Real robots
Issue Date: 2017
Publisher: The Royal Society
Abstract: Online evolution of behavioural control on real robots is an open-ended approach to autonomous learning and adaptation: robots have the potential to automatically learn new tasks and to adapt to changes in environmental conditions, or to failures in sensors and/or actuators. However, studies have so far almost exclusively been carried out in simulation because evolution in real hardware has required several days or weeks to produce capable robots. In this article, we successfully evolve neural network-based controllers in real robotic hardware to solve two single-robot tasks and one collective robotics task. Controllers are evolved either from random solutions or from solutions pre-evolved in simulation. In all cases, capable solutions are found in a timely manner (1 h or less). Results show that more accurate simulations may lead to higher-performing controllers, and that completing the optimization process in real robots is meaningful, even if solutions found in simulation differ from solutions in reality. We furthermore demonstrate for the first time the adaptive capabilities of online evolution in real robotic hardware, including robots able to overcome faults injected in the motors of multiple units simultaneously, and to modify their behaviour in response to changes in the task requirements. We conclude by assessing the contribution of each algorithmic component on the performance of the underlying evolutionary algorithm.
Peer reviewed: yes
URI: http://hdl.handle.net/10071/14922
DOI: 10.1098/rsos.160938
ISSN: 2054-5703
Ciência-IUL: https://ciencia.iscte-iul.pt/id/ci-pub-43608
Accession number: WOS:000406670000035
Appears in Collections:IT-RI - Artigo em revista internacional com arbitragem científica

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