综述学习型机器人安全控制方法，兼顾性能与可靠性。
Survey on safe robot control via learning
- 整合经典控制与学习方法，构建安全约束框架
- 提出多场景下鲁棒性与安全性协同优化方案
- 适合机器人安全系统研发者参考
控制系统是现代技术基础设施的关键组成部分，涵盖航空航天、医疗等多个领域。本综述探讨了安全机器人学习的研究现状，分析了如何在保证高性能控制的同时严格满足安全约束。通过考察经典控制技术、基于学习的方法以及嵌入式系统设计，研究旨在理解如何在复杂操作环境中开发出能够预防危险状态且保持最优性能的机器人系统。
原文摘要 · Abstract (English)
Control systems are critical to modern technological infrastructure, spanning industries from aerospace to healthcare. This survey explores the landscape of safe robot learning, investigating methods that balance high-performance control with rigorous safety constraints. By examining classical control techniques, learning-based approaches, and embedded system design, the research seeks to understand how robotic systems can be developed to prevent hazardous states while maintaining optimal performance across complex operational environments.
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