Designing robots by hand is often both costly and time consuming. 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 the exploration of morphologies using a more indirect approach. We explore how changing the environment the agent locomotes in affects the convergence of morphologies. Inspired by POET, an algorithm which evolves environments open-endedly, we create POET-M, an expansion of POET which includes evolution of morphologies. We compare the morphological change and diversity of agents evolving in a static environment, a curriculum of hand crafted environments, and in POET-M. We show that the agents experience increased morphological change in response to environmental change, and that agents evolving in an open-ended environment exhibit larger morphological diversity in the population than agents evolving in a static flat environment or a hand crafted curriculum of environments. POET-M proved capable of creating a curricula of environments which encouraged both diversity and quality in the population. This might suggest that the open-endedly evolving environments in POET-M act as stepping stones for the agents, enabling the morphology to escape local optima and continue evolving past the early stages of the evolution.