Data-driven learning based methods have recently been particularly successful
at learning robust locomotion controllers for a variety of unstructured
terrains. Prior work has shown that incorporating good locomotion priors in the
form of trajectory generators (TGs) is effective at efficiently learning
complex locomotion skills. However, defining a good, single TG as
tasks/environments become increasingly more complex remains a challenging
problem as it requires extensive tuning and risks reducing the effectiveness of
the prior. In this paper, we present Evolved Environmental Trajectory
Generators (EETG), a method that learns a diverse set of specialised locomotion
priors using Quality-Diversity algorithms while maintaining a single policy
within the Policies Modulating TG (PMTG) architecture. The results demonstrate
that EETG enables a quadruped robot to successfully traverse a wide range of
environments, such as slopes, stairs, rough terrain, and balance beams. Our
experiments show that learning a diverse set of specialized TG priors is
significantly (5 times) more efficient than using a single, fixed prior when
dealing with a wide range of environments.