用质量多样性算法生成多样且难测的地形，发现机器人控制器弱点。
Generating Diverse Challenging Terrains for Legged Robots Using Quality-Diversity Algorithm
- 基于质量多样性算法生成挑战性地形，覆盖多种失败模式。
- 生成地形使机器人在模拟中出现多种未预期的失效情况。
- 结果可用于改进强化学习控制器，适合机器人测试与训练。
尽管近年足式机器人取得显著进展，但确保其在非结构化地形上控制系统的鲁棒性仍具挑战。这需要生成多样化且具有挑战性的非结构化地形来测试机器人并发现其弱点，但该方向在文献中仍研究不足。本文提出一种质量多样性框架，用于生成能暴露足式机器人控制器缺陷的多样化且具有挑战性的地形。该方法应用于模拟的双足与四足机器人，生成一个优化后的地形档案，可从不同角度挑战控制器。定量与定性分析表明，生成的地形档案确实包含机器人难以穿越的场景，并呈现多种故障模式。有趣的是，部分失败情况并非预期中的典型情形。实验还表明，这些生成的地形可用于改进基于强化学习的控制器。
原文摘要 · Abstract (English)
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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