Formal Methods in Robot Policy Learning: A Survey on Current Techniques and Future Directions

TMLR Paper5454 Authors

Abstract:

As hardware and software systems have grown in complexity, formal methods have been indispensable tools for (1) rigorously specifying acceptable behaviors, (2) synthesizing programs to meet these specifications, and (3) 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 are integrated into robot policy learning. We organize our discussion around three key pillars: specification, synthesis, and verification of learned policies. For each, 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.

Submission Length: Long submission (more than 12 pages of main content)

Assigned Action Editor: ~Oleg_Arenz1

Submission Number: 5454