Rapid and Reliable Quadruped Motion Planning with Omnidirectional Jumping

TL;DR

This paper introduces a hierarchical planning framework enabling quadruped robots to perform omnidirectional jumps efficiently, combining real-time trajectory optimization with a jump feasibility classifier for reliable, goal-oriented long-horizon motion planning.

Contribution

The work presents a novel hierarchical approach integrating real-time jump trajectory optimization with a feasibility classifier for omnidirectional jumping in quadruped robots.

Findings

Successfully deployed on Mini Cheetah Vision robot

Achieved reliable, goal-oriented omnidirectional jumps

Expanded robot mobility beyond sagittal or frontal plane jumps

Abstract

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.