Towards Quadrupedal Jumping and Walking for Dynamic Locomotion using Reinforcement Learning

TL;DR

This paper introduces a reinforcement learning framework for training a quadruped robot to perform precise, high-performance jumping and walking, achieving significant jumps and versatile locomotion across terrains.

Contribution

It develops a curriculum-based RL approach with reward densification and reference state initialization for effective dynamic jumping and walking policies.

Findings

Horizontal jumps up to 1.25 m with centimeter accuracy

Vertical jumps up to 1.0 m

Effective crossing of the Sim2Real gap

Abstract

This paper presents a curriculum-based reinforcement learning framework for training precise and high-performance jumping policies for the robot `Olympus'. Separate policies are developed for vertical and horizontal jumps, leveraging a simple yet effective strategy. First, we densify the inherently sparse jumping reward using the laws of projectile motion. 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.