Formal Methods for Safe and Interpretable Reinforcement Learning

Autonomous robots and other large dynamical systems often have complex, uncertain or partly unknown models that limit traditional model-based control. Machine learning and reinforcement learning offer adaptable decision-making methods, but their interpretability and guarantees of safe, correct behavior remain limited. This talk brings formal methods together with control theory and learning: expressive specification languages capture objectives and safety requirements, while verification, synthesis and proofs establish desired behavior. Examples from autonomous driving and robotic manipulation illustrate controllers designed to scale and adapt while remaining safe and interpretable.