As long as the mutation rate is greater than zero, there is always the possibility that a creatures manages to mutate its way out of a local maximum. I haven't seen that happen myself, but to be fair, the majority of my time on this project was spent implementing it and not experimenting with long-running simulations.
What I would recommend is to try to play around with different selection, recombination and mutation algorithms and to increase the mutation rate when the fitness starts to stagnate. The settings that lead to the most randomized simulation behaviour (which can be exactly what you need if you're stuck in a local maximum) are
uniform selection
uniform recombination and
global mutation
with a mutation rate of 100%.
As far as the network size is concerned, I have seen some very interesting results with networks about the size that you have chosen. Sometimes with even more intermediate nodes (but not with more layers). I haven't seen too many successful simulation results with very deep networks. You might get lucky and immediately start off with one that works really well, but it's a lot more unlikely than with smaller networks.
The network inputs include the average rotation of all bones which can give the creature some sense of rotation. Additionally, the creatures cast rays into the world in order to learn about the distance from their surroundings. Some of these rays have a fixed direction (e.g. up and forward) but one of them is controlled by a neural network output of the creature itself. It could therefore also use the information it gets from these rays in order to determine its current orientation in space.