  Quality-Diversity optimization is a new family of optimization algorithms
that, instead of searching for a single optimal solution to solving a task,
searches for a large collection of solutions that all solve the task in a
different way. This approach is particularly promising for learning behavioral
repertoires in robotics, as such a diversity of behaviors enables robots to be
more versatile and resilient. However, these algorithms require the user to
manually define behavioral descriptors, which is used to determine whether two
solutions are different or similar. The choice of a behavioral descriptor is
crucial, as it completely changes the solution types that the algorithm
derives. In this paper, we introduce a new method to automatically define this
descriptor by combining Quality-Diversity algorithms with unsupervised
dimensionality reduction algorithms. This approach enables robots to
autonomously discover the range of their capabilities while interacting with
their environment. The results from two experimental scenarios demonstrate that
robot can autonomously discover a large range of possible behaviors, without
any prior knowledge about their morphology and environment. Furthermore, these
behaviors are deemed to be similar to handcrafted solutions that uses domain
knowledge and significantly more diverse than when using existing unsupervised
methods.
