Events

Towards Generalizable Safety for Robot Autonomy: Integrating Model-Based Analysis with Data-Driven Approaches

Speaker: Jason Jangho Choi

Location: TBA

Date: Wednesday, March 5, 2025

Location: 6MTC MakerEvent Space

Ensuring safety is a fundamental prerequisite for deploying robots in real-world environments, from self-driving cars to autonomous air taxis. Achieving autonomy at a societal scale requires a foundation built on rigorous safety guarantees. However, existing safety frameworks often struggle in complex, unforeseen scenarios due to their reliance on human expertise and trial-and-error methods. My research aims to develop generalizable safety frameworks with principled design methodologies and assurance mechanisms that reduce the need for extensive engineering effort. In this talk, I will introduce data-driven safety frameworks for robot autonomy, emphasizing their control-theoretic foundations and extensions to data-driven and learning-enabled methods. First, I will demonstrate how two key safety concepts in control theory—control barrier functions (CBFs) and Hamilton-Jacobi reachability—can be unified into a single framework via the control barrier-value function (CBVF) and discuss its implications for learning-enabled methods. Next, I will show its application to achieving safe decentralized autonomy in advanced air mobility (AAM) operations, integrating CBVF-based safety filters with multi-agent reinforcement learning (MARL). Finally, I will introduce a novel data-driven Hamiltonian approach, enabling the direct construction of certifiable safe sets from system trajectory data. A key application of this framework is in automating flight test procedures for air taxi vehicles with complex nonlinear aerodynamics, providing a guaranteed approach to conducting experiments safely while expanding the verified safe operating region.