Description
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.