  Dynamic traversal of uneven terrain is a major objective in the field of
legged robotics. The most recent model predictive control approaches for these
systems can generate robust dynamic motion of short duration; however, planning
over a longer time horizon may be necessary when navigating complex terrain. A
recently-developed framework, Trajectory Optimization for Walking Robots
(TOWR), computes such plans but does not guarantee their reliability on real
platforms, under uncertainty and perturbations. We extend TOWR with analytical
costs to generate trajectories that a state-of-the-art whole-body tracking
controller can successfully execute. To reduce online computation time, we
implement a learning-based scheme for initialization of the nonlinear program
based on offline experience. The execution of trajectories as long as 16
footsteps and 5.5 s over different terrains by a real quadruped demonstrates
the effectiveness of the approach on hardware. This work builds toward an
online system which can efficiently and robustly replan dynamic trajectories.
