Dynamic jumping with multi-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 trajectories further complicates the problem. In this work, we present a novel 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 by a high-level planner to produce paths that are both dynamically feasible and also robust to variability in hardware trajectory realizations. We deploy our framework on the Mini Cheetah Vision quadruped, demonstrating robot's ability to generate and execute reliable, goal-oriented paths that involve forward, lateral, and rotational jumps onto surfaces 1.35 times taller than robot's nominal hip height. The ability to plan through omnidirectional jumping greatly expands robot's mobility relative to planners that restrict jumping to the sagittal or frontal planes.

翻译：与多脚机器人一起动态跳跃给规划和控制带来了一个具有挑战性的难题。 制定跳跃优化以允许快速在线执行是困难的; 高效地使用这种能力来生成长视轨迹使问题更加复杂。 在这项工作中, 我们提出了一个新的等级规划框架来解决这个问题。 我们首先为进行全向跳动制定实时可移动的轨迹优化。 我们然后将这一优化的结果嵌入一个低维跳跃可行性分类器中。 这个分类器被一个高级规划员利用来生成既动态可行又对硬件轨迹实现的变异性都十分强大的路径。 我们将我们的框架运用在“ Mini Cheetah Vision ” 上, 展示机器人生成和执行可靠、 面向目标的路径的能力, 这些路径涉及前向、 横向和旋转性跳到表面的高度, 比机器人名义的时高1.35倍。 通过全向跳跃跃来规划的能力大大扩展机器人相对于限制跳跃到斜向或前方平面的策划者的流动性。