Collaborative Research: SLES: Guaranteed Tubes for Safe Learning across Autonomy Architectures

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The autonomous systems that operate by themselves (e.g., autonomous cars and delivery drones) expose safety challenges dependent upon the complexity of the missions and environments. Although such systems have demonstrated the ability to learn and adapt on their own, ensuring the safety of these systems is not trivial. Safety can be endangered due to various factors, including but not limited to unexpected changes, inclement weather conditions, or unknown obstacles. The onboard learning solutions of these systems have mostly been used in non-critical situations like computer games, where safety concerns are minimal. This proposal aims to address the urgent need for end-to-end safety in learning-enabled systems across various application scenarios, e.g., self-driving cars and flying vehicles in urban air mobility. We propose a novel solution called "Data-enabled Simplex" or "DeSimplex.” DeSimplex is built on solid mathematical principles and systematic methods for collecting data and using the data for the system’s performance improvement. It provides a framework that can be proven to be safe and allows learning-enabled systems to adjust and perform well even when faced with extreme events, or environmental hazards. The proposed work is crucial for wider applications that involve the safe and efficient operation of autonomous systems in unpredictable, demanding physical environments, including autonomous cars and flying vehicles of 3D urban air mobility.The proposed work lays the groundwork for advancing the comprehension of safety in end-to-end learning-enabled systems, a foundational problem in cyber-physical systems, robotics, and machine learning. We aim to pursue the following two interconnected research thrusts: (i) improving high-performance autonomy with reliable uncertainty quantification methods to ensure data-driven adaptability and precise measurement of uncertainties and (ii) developing high-assurance autonomy architectures and establishing switching rules for verifiable observability and controllability. A framework will be developed that combines the strengths of high-performance and high-assurance autonomy, facilitating adaptive learning, accurate uncertainty quantification, and verifiable safety measures. Novel methods will be developed for (i) on-policy, closed-loop learning to boost performance, (ii) reliable uncertainty quantification to provide data-driven adaptability, and (iii) verifiable observability and controllability at the system level. A systematic, dual-strategy approach will be pursued for safe data collection for the proposed high-performance autonomy to reconcile the three desired properties for data: safety, on-policy, and closed-loop. The proposed framework will be validated in a rigorous procedure from modular simulation testing to integration and deployment on real aerial and ground vehicles.This research is supported by a partnership between the National Science Foundation and Open Philanthropy.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.