  Designing robots by hand can be costly and time consuming, especially if the
robots have to be created with novel materials, or be robust to internal or
external changes. In order to create robots automatically, without the need for
human intervention, it is necessary to optimise both the behaviour and the body
design of the robot. However, when co-optimising the morphology and controller
of a locomoting agent the morphology tends to converge prematurely, reaching a
local optimum. Approaches such as explicit protection of morphological
innovation have been used to reduce this problem, but it might also be possible
to increase exploration of morphologies using a more indirect approach. We
explore how changing the environment, where the agent locomotes, affects the
convergence of morphologies. The agents' morphologies and controllers are
co-optimised, while the environments the agents locomote in are evolved
open-endedly with the Paired Open-Ended Trailblazer (POET). We compare the
diversity, fitness and robustness of agents evolving in environments generated
by POET to agents evolved in handcrafted curricula of environments. Our agents
each contain of a population of individuals being evolved with a genetic
algorithm. This population is called the agent-population. We show that
agent-populations evolving in open-endedly evolving environments exhibit larger
morphological diversity than agent-populations evolving in hand crafted
curricula of environments. POET proved capable of creating a curriculum of
environments which encouraged both diversity and quality in the populations.
This suggests that POET may be capable of reducing premature convergence in
co-optimisation of morphology and controllers.
