  Quality-Diversity algorithms refer to a class of evolutionary algorithms
designed to find a collection of diverse and high-performing solutions to a
given problem. In robotics, such algorithms can be used for generating a
collection of controllers covering most of the possible behaviours of a robot.
To do so, these algorithms associate a behavioural descriptor to each of these
behaviours. Each behavioural descriptor is used for estimating the novelty of
one behaviour compared to the others. In most existing algorithms, the
behavioural descriptor needs to be hand-coded, thus requiring prior knowledge
about the task to solve. In this paper, we introduce: Autonomous Robots
Realising their Abilities, an algorithm that uses a dimensionality reduction
technique to automatically learn behavioural descriptors based on raw sensory
data. The performance of this algorithm is assessed on three robotic tasks in
simulation. The experimental results show that it performs similarly to
traditional hand-coded approaches without the requirement to provide any
hand-coded behavioural descriptor. In the collection of diverse and
high-performing solutions, it also manages to find behaviours that are novel
with respect to more features than its hand-coded baselines. Finally, we
introduce a variant of the algorithm which is robust to the dimensionality of
the behavioural descriptor space.
