Generating Diverse Challenging Terrains for Legged Robots Using Quality-Diversity Algorithm

TL;DR

This paper introduces a Quality-Diversity algorithm to generate diverse, challenging terrains for testing and improving legged robot controllers, revealing vulnerabilities and failure modes in simulation.

Contribution

It presents a novel framework for generating challenging terrains that uncover weaknesses in legged robot controllers, aiding robustness testing and controller improvement.

Findings

Generated terrains reveal unexpected failure modes.

Terrains effectively challenge both bipedal and quadruped robots.

Use of terrains improves RL-based controller robustness.

Abstract

While legged robots have achieved significant advancements in recent years, ensuring the robustness of their controllers on unstructured terrains remains challenging. It requires generating diverse and challenging unstructured terrains to test the robot and discover its vulnerabilities. This topic remains underexplored in the literature. This paper presents a Quality-Diversity framework to generate diverse and challenging terrains that uncover weaknesses in legged robot controllers. Our method, applied to both simulated bipedal and quadruped robots, produces an archive of terrains optimized to challenge the controller in different ways. Quantitative and qualitative analyses show that the generated archive effectively contains terrains that the robots struggled to traverse, presenting different failure modes. Interesting results were observed, including failure cases that were not necessarily expected. Experiments show that the generated terrains can also be used to improve RL-based controllers.