  Taking inspiration from how the brain coordinates multiple learning systems
is an appealing strategy to endow robots with more flexibility. One of the
expected advantages would be for robots to autonomously switch to the least
costly system when its performance is satisfying. However, to our knowledge no
study on a real robot has yet shown that the measured computational cost is
reduced while performance is maintained with such brain-inspired algorithms. We
present navigation experiments involving paths of different lengths to the
goal, dead-end, and non-stationarity (i.e., change in goal location and
apparition of obstacles). We present a novel arbitration mechanism between
learning systems that explicitly measures performance and cost. We find that
the robot can adapt to environment changes by switching between learning
systems so as to maintain a high performance. Moreover, when the task is
stable, the robot also autonomously shifts to the least costly system, which
leads to a drastic reduction in computation cost while keeping a high
performance. Overall, these results illustrates the interest of using multiple
learning systems.
