  Optimizing gait stability for legged robots is a difficult problem. Even on
level surfaces, effectively traversing across different textures (e.g., carpet)
rests on dynamically tuning parameters in multidimensional space. Inspired by
biology, evolutionary algorithms (EA) remain an attractive solution for
feasibly implementing robotic locomotion with both energetic economy and rapid
parameter convergence. Here, we leveraged this class of algorithms to evolve a
stable hexapod gait controller capable of traversing uneven terrain and
obstacles. Gait parameters were evolved in a rigid body dynamics simulation on
an 8 x 3 meter obstacle course comprised of random step field, linear obstacles
and inclined surfaces. Using a fitness function that jointly optimized
locomotion velocity and stability, we found that multiple successful gait
parameter evolutions yielded specialized functionality for each leg. Specific
gait parameters were identified as critical to developing a rough terrain gait.
