Cat-like Jumping and Landing of Legged Robots in Low-gravity Using Deep Reinforcement Learning

TL;DR

This paper demonstrates that deep reinforcement learning can be used to train legged robots for complex jumping and landing tasks in low-gravity environments, with successful transfer from simulation to real-world experiments.

Contribution

The study introduces a deep reinforcement learning approach for low-gravity legged locomotion, including sim-to-real transfer for space exploration robots.

Findings

Successful training of quadruped jumping policies in simulation

Effective sim-to-real transfer to the SpaceBok robot

Repetitive controlled jumping and landing achieved in low-gravity conditions

Abstract

In this article, we show that learned policies can be applied to solve legged locomotion control tasks with extensive flight phases, such as those encountered in space exploration. Using an off-the-shelf deep reinforcement learning algorithm, we trained a neural network to control a jumping quadruped robot while solely using its limbs for attitude control. We present tasks of increasing complexity leading to a combination of three-dimensional (re-)orientation and landing locomotion behaviors of a quadruped robot traversing simulated low-gravity celestial bodies. We show that our approach easily generalizes across these tasks and successfully trains policies for each case. Using sim-to-real transfer, we deploy trained policies in the real world on the SpaceBok robot placed on an experimental testbed designed for two-dimensional micro-gravity experiments. The experimental results demonstrate that repetitive, controlled jumping and landing with natural agility is possible.