CAJun: Continuous Adaptive Jumping using a Learned Centroidal Controller

TL;DR

CAJun is a hierarchical framework combining reinforcement learning and optimal control that enables legged robots to perform continuous, adaptive jumps with high robustness and transferability to real robots, achieving wider jumps than previous methods.

Contribution

The paper introduces CAJun, a novel hierarchical learning and control system that integrates RL and optimal control for continuous adaptive jumping in legged robots.

Findings

Achieves continuous jumps with adaptive distances after 20 minutes of training.

Enables jumping across gaps up to 70cm wide, surpassing existing methods by over 40%.

Demonstrates effective sim-to-real transfer on a Go1 robot.

Abstract

We present CAJun, a novel hierarchical learning and control framework that enables legged robots to jump continuously with adaptive jumping distances. CAJun consists of a high-level centroidal policy and a low-level leg controller. In particular, we use reinforcement learning (RL) to train the centroidal policy, which specifies the gait timing, base velocity, and swing foot position for the leg controller. The leg controller optimizes motor commands for the swing and stance legs according to the gait timing to track the swing foot target and base velocity commands using optimal control. Additionally, we reformulate the stance leg optimizer in the leg controller to speed up policy training by an order of magnitude. Our system combines the versatility of learning with the robustness of optimal control. By combining RL with optimal control methods, our system achieves the versatility of learning while enjoys the robustness from control methods, making it easily transferable to real robots. We show that after 20 minutes of training on a single GPU, CAJun can achieve continuous, long jumps with adaptive distances on a Go1 robot with small sim-to-real gaps. Moreover, the robot can jump across gaps with a maximum width of 70cm, which is over 40% wider than existing methods.