用对抗生成场景训练机器人安全策略，高效发现高风险边缘案例。
Learning of Robot Safety Policies via Adversarial Synthetic Scenarios
- 红蓝双队对抗生成危险场景，动态优化安全策略
- 通过对抗机制发现随机模拟难捕捉的高危边缘案例
- 适合研究物理AI系统安全与可信赖智能体的学者
本文提出一种基于代理博弈的生成式框架，用于通过合成场景进行危险感知的机器人安全策略学习。将场景生成建模为两个代理间的对抗游戏：红队通过构建危险情境探索潜在故障空间，蓝队则逐步优化安全策略以防范这些威胁。该迭代过程能够高效发现随机仿真或人工枚举难以覆盖的高风险边缘案例。结合经典风险建模、对抗性场景生成与现代学习范式，本工作为复杂真实环境中的物理人工智能系统嵌入安全性提供了可扩展路径。论文描述的是正在进行的工作，贡献在于问题定义和所提出的解决方案架构。
原文摘要 · Abstract (English)
In this work, we propose an agentic gamification framework for hazard-informed learning of robot safety policies through synthetic scenarios. We model scenario generation as an adversarial game between two agents: a Red Team that explores the space of potential failures by constructing hazardous situations, and a Blue Team that incrementally refines safety policies to prevent them. This iterative process enables efficient discovery of high-risk edge cases that are unlikely to be captured through random simulation or manual enumeration. By combining classical risk modeling with adversarial scenario generation and modern learning paradigms, this work provides a scalable pathway for embedding safety into Physical AI systems operating in complex real-world environments. The paper describes ongoing work. The contribution is a problem formulation and a proposed solution architecture.
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。