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

2025/06/02 by Arthur Esquerre-Pourtère, Minsoo Kim, Esquerre-Pourtère, Arthur +3
Computer Science · Engineering · #FOS: Computer and information sciences #Modular Robots and Swarm Intelligence #Neural and Evolutionary Computing (cs.NE) #Robotic Locomotion and Control #Robotic Path Planning Algorithms #Robotics (cs.RO)

paper · doi:10.48550/arxiv.2506.01362

openalex publication_date 2025/06/02 · openalex created_date 2025/10/10 · openalex updated_date 2026/07/28

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.

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Related

• Generating a Terrain-Robustness Benchmark for Legged Locomotion: A Prototype via Terrain Authoring and Active Learning
• MARG: MAstering Risky Gap Terrains for Legged Robots with Elevation Mapping
• Parkour in the Wild: Learning a General and Extensible Agile Locomotion Policy Using Multi-expert Distillation and RL Fine-tuning
• GenTe: Generative Real-world Terrains for General Legged Robot Locomotion Control
• Learning to enhance multi-legged robot on rugged landscapes