Towards Quadrupedal Jumping and Walking for Dynamic Locomotion using Reinforcement Learning

Abstract-- This paper presents a curriculum-based reinforcement learning framework for training precise and high-performance jumping policies for the robot'Jumper'. Separate policies are developed for vertical and horizontal jumps, leveraging a simple yet effective strategy. Next, a reference state initialization scheme is employed to accelerate the exploration of dynamic jumping behaviors without reliance on reference trajectories. We also present a walking policy that, when combined with the jumping policies, unlocks versatile and dynamic locomotion capabilities. Comprehensive testing validates walking on varied terrain surfaces and jumping performance that exceeds previous works, effectively crossing the Sim2Real gap. Experimental validation demonstrates horizontal jumps up to 1.25 m with centimeter accuracy and vertical jumps up to 1.0 m. Additionally, we show that with only minor modifications, the proposed method can be used to learn omnidirectional jumping. I. INTRODUCTION Quadruped robots can navigate complex terrains and overcome obstacles not only through walking but also through powerful jumps. The combination of robust walking and precise jumping capabilities is particularly valuable for planetary exploration [1], [2].