CAREER: Towards Hierarchical and Provably Safe Control for Learning-Enabled Autonomous Systems

This Faculty Early Career Development Program (CAREER) grant will fund research that enhances the reliability, trustworthiness, and societal acceptance of autonomous systems that rely on machine learning-enabled technologies, thereby promoting the progress of science, and advancing the national prosperity and welfare. Autonomous robotic systems, such as self-driving cars and drones, are shaping the nation's future insofar as the transportation, logistics, and service segments of the economy are concerned. Artificial neural networks have become an indispensable component of modern autonomous systems, especially in their perception and control pipelines. However, neural networks are complex, difficult to analyze, and sensitive to input perturbations or adversarial attacks. This renders their rigorous analysis and design very challenging. Thus, despite the continued optimism and tremendous technological progress in recent years, truly autonomous systems remain elusive because of outstanding safety and reliability concerns. This project overcomes these concerns by establishing a rigorous methodological framework and efficient algorithms for the analysis, verification, motion planning, and control design of safety-critical dynamic systems with learning-enabled components. It demonstrates how this framework enables provable performance guarantees for safe and reliable operation. Through close integration of research, education, and outreach, the project aims to leverage knowledge discovery to stimulate teaching and learning, use inspired teaching to encourage excitement in research, and make newly generated knowledge accessible to the public. This is accomplished through active learning-based design of a course on safety control in robotics, by engaging students from underrepresented groups in research and organizing K-12 summer workshops with hands-on robotics activities, and by increasing public literacy, awareness, and trust in safety-related technologies for autonomous systems. This research aims to develop the foundations of a mathematically rigorous framework for the multi-rate and provably safe motion planning and control of autonomous systems with neural network components. It achieves this aim by investigating constrained zonotope- and hybrid zonotope-based algorithms for computing over-approximated reachable sets for neural feedback systems with a tunable trade-off between computational efficiency and approximation accuracy; robust quadratic program-based methods for designing provably safe, periodic event-triggered tracking controllers; second-order cone program-based trajectory planning methods for neural feedback systems with continuous-time safety guarantees; and provably safe multi-rate planning and control algorithms with an assume-guarantee contract between the planning and tracking layers. Verification and validation of the theoretical results will be performed using high-fidelity vehicle dynamics software simulations and with physical experiments on two lab-based robotic platforms. This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.