机器人策略学习与验证中的形式方法：对当前技术与未来方向的综述
Formal Methods in Robot Policy Learning and Verification: A Survey on Current Techniques and Future Directions
AI总结：
本文综述了形式方法在机器人策略学习与验证中的应用，探讨了当前技术及未来发展方向，旨在提升机器人系统的安全性和正确性。
摘要：
随着硬件和软件系统复杂性的增加，形式方法已成为严格指定可接受行为、合成满足这些规范的程序以及验证现有程序正确性的不可或缺工具。在机器人领域，类似的复杂性上升趋势也出现了，这主要由深度学习的广泛应用所驱动。尽管这种转变使开发高性能机器人策略成为可能，但将其作为深度神经网络实现却给传统的形式分析带来了挑战，导致模型缺乏灵活性、易碎且难以解释。为此，机器人社区引入了新的形式和半形式方法，以支持对复杂目标的精确规范、引导学习过程以实现这些目标，并能够验证学习的策略以符合这些目标。在本次综述中，我们提供了对最近机器人学习研究中形式方法应用的全面概述。我们的讨论围绕两个支柱展开：策略学习和策略验证。对于两者，我们突出代表性的技术，比较它们的可扩展性和表达性，并总结它们如何有助于有意义地提高现实机器人安全性和正确性。最后，我们讨论了实现该目标所面临的剩余障碍以及推动形式方法在机器人学习中进一步发展的有希望的方向。
英文摘要：
As hardware and software systems have grown in complexity, formal methods have been indispensable tools for rigorously specifying acceptable behaviors, synthesizing programs to meet these specifications, and validating the correctness of existing programs. In the field of robotics, a similar trend of rising complexity has emerged, driven in large part by the adoption of deep learning. While this shift has enabled the development of highly performant robot policies, their implementation as deep neural networks has posed challenges to traditional formal analysis, leading to models that are inflexible, fragile, and difficult to interpret. In response, the robotics community has introduced new formal and semi-formal methods to support the precise specification of complex objectives, guide the learning process to achieve them, and enable the verification of learned policies against them. In this survey, we provide a comprehensive overview of how formal methods have been used in recent robot learning research. We organize our discussion around two pillars: policy learning and policy verification. For both, we highlight representative techniques, compare their scalability and expressiveness, and summarize how they contribute to meaningfully improving realistic robot safety and correctness. We conclude with a discussion of remaining obstacles for achieving that goal and promising directions for advancing formal methods in robot learning.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
