CPS: SMALL: Formal Methods for Safe, Efficient, and Transferable Learning-enabled Autonomy

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Deep Reinforcement Learning (RL) has emerged as prominent tool to control cyber-physical systems (CPS) with highly non-linear, stochastic, and unknown dynamics. Nevertheless, our current lack of understanding of when, how, and why RL works necessitates the need for new synthesis and analysis tools for safety-critical CPS driven by RL controllers; this is the main scope of this project. The primary focus of this research is on mobile robot systems. Such CPS are often driven by RL controllers due to their inherent complex - and possibly uncertain/unknown - dynamics, unknown exogenous disturbances, or the need for real-time decision making. Typically, RL-based control design methods are data inefficient, they cannot be safely transferred to new mission & safety requirements or new environments, while they often lack performance guarantees. This research aims to address these limitations resulting in a novel paradigm in safe autonomy for CPS with RL controllers. Wide availability of the developed autonomy methods can enable safety-critical applications for CPS with significant societal impact on, e.g., environmental monitoring, infrastructure inspection, autonomous driving, and healthcare. The broader impacts of this research include its educational agenda involving K-12, undergraduate and graduate level education.To achieve the research goal of safe, efficient, and transferable RL, three tightly coupled research thrusts are pursued: (i) accelerated & safe reinforcement learning for temporal logic control objectives; (ii) safe transfer learning for temporal logic control objectives; (iii) compositional verification of temporal logic properties for CPS with NN controllers. The technical approach in these thrusts relies on tools drawn from formal methods, machine learning, and control theory and requires overcoming intellectual challenges related to integration of computation, control, and sensing. The developed autonomy methods will be validated and demonstrated on mobile aerial and ground robots in autonomous surveillance, delivery, and mobile manipulation tasks.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.