用多样性优化生成多样真实场景，发现机器人系统隐藏缺陷。
Algorithmic Scenario Generation as Quality Diversity Optimization
- 将场景生成建模为质量多样性优化问题
- 发现多种真实且具挑战性的交互场景
- 适合机器人安全测试与验证的研究者
随着机器人和自主代理与人类交互的复杂性增加，部署前系统性测试的需求愈发紧迫。本文提出一个通用框架来解决该问题，阐述了在框架各组件研究中获得的洞察，并展示如何整合这些组件，从而发现一系列多样化、真实且具有挑战性的场景，揭示了已部署机器人系统与人类交互时此前未知的失败模式。
原文摘要 · Abstract (English)
The increasing complexity of robots and autonomous agents that interact with people highlights the critical need for approaches that systematically test them before deployment. This review paper presents a general framework for solving this problem, describes the insights that we have gained from working on each component of the framework, and shows how integrating these components leads to the discovery of a diverse range of realistic and challenging scenarios that reveal previously unknown failures in deployed robotic systems interacting with people.
Thank you to arXiv for use of its open access interoperability. PaperDance 不是 arXiv 官方产品；中文卡片由大模型生成，请以原文为准。