Stanford Robotics Seminar ENGR319 | Autumn 2026 | Bridging Rigorous Methods with Machine Learning

For more information about Stanford’s graduate programs, visit: https://stanford.io/grad-ed-engr319-1002 October 2, 2026 This seminar covers: • Why learning-based controllers struggle with real-world deployment: we can’t guarantee a learned policy is safe, and we can’t efficiently find the rare scenarios where it fails • A new approach that closes the loop between synthesis (building safe policies) and falsification (finding where they break) • How neural certificates can be formulated as value functions in reinforcement learning, producing control policies with built-in safety and stability guarantees — turning safety into a structural property of the policy rather than a soft penalty • An adaptive test-case generation method using Bayesian active learning to discover failure scenarios that are both likely to reveal problems and useful for broader coverage • How this method can predict failures from limited hardware demonstrations • How it supports scalable, evolving experimental design for system-level testing Follow along with the seminar schedule: https://stanfordasl.github.io/robotics_seminar/archive/ Chuchu Fan is an Associate Professor (pre-tenure) in the Department of Aeronautics and Astronautics (AeroAstro) and the Laboratory for Information and Decision Systems (LIDS) at MIT.