  Dynamic jumping with legged robots poses a challenging problem in planning
and control. Formulating the jump optimization to allow fast online execution
is difficult; efficiently using this capability to generate long-horizon motion
plans further complicates the problem. In this work, we present a hierarchical
planning framework to address this problem. We first formulate a real-time
tractable trajectory optimization for performing omnidirectional jumping. We
then embed the results of this optimization into a low dimensional jump
feasibility classifier. This classifier is leveraged to produce geometric
motion plans that select dynamically feasible jumps while mitigating the
effects of the process noise. We deploy our framework on the Mini Cheetah
Vision quadruped, demonstrating the robot's ability to generate and execute
reliable, goal-oriented plans that involve forward, lateral, and rotational
jumps onto surfaces as tall as the robot's nominal hip height. The ability to
plan through omnidirectional jumping greatly expands the robot's mobility
relative to planners that restrict jumping to the sagittal or frontal planes.
