Data from: Evolutionary online behaviour learning and adaptation in real robots

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.