Title: Control-Oriented Learning for Same-Day Autonomy

Abstract: Autonomous systems are expected to adapt to new tasks and environments with little prior data, operate within the constraints of physical laws, and satisfy rigorous specifications for safety and performance. Meeting these demands requires moving beyond purely data-driven learning to hybrid methods that integrate control-theoretic reasoning, physics-based models, and formal specifications with modern machine learning. This talk will present a control-oriented perspective on learning that enables autonomy at operationally relevant timescales. I will highlight recent results showing how embedding physical knowledge and structured representations into learning architectures yields dramatic gains in data efficiency, generalization, and verifiability. These methods support on-the-fly adaptation and provide pathways to performance guarantees, even when data are scarce and environments are uncertain. The talk will conclude with a broader outlook on how control, learning, and formal methods together can bring trustworthy, rapidly deployable autonomy within reach.