CAREER: A Skill-Driven Cooperative Learning Framework for Cyber-Physical Autonomy

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This project investigates new reinforcement learning (RL) approaches for cyber-physical autonomy to bridge the gap between current intelligent systems and human-level intelligence. The nature of many cyber-physical systems (CPS) is distributed, heterogeneous, and high-dimensional, making the hand-coded functions and task-specific information hard to design in the learning scheme. Large amount of training data is often required for achieving the desired performance, however this limits the generalization to other tasks. Hence, this project is to explore the new RL strategies to enable CPS with the capabilities of autonomous learning and generalization to rapidly adapt in unknown situations that were not assumed in the design phase. The results are expected to transform how agents interact in high-dimensional and heterogeneous environment, and therefore could potentially provide in-depth findings for exploring creativity in frontier Artificial Intelligence techniques. The goal of this project is to advance foundational knowledge and scientific methodologies of reinforcement learning for generalization and scalability in CPS. Motivated by the recent research in neurobiology and psychology, this project will design a new skill-driven intelligent control approach for CPS that can learn more expressive extended skills to autonomously and adaptively handle unknown situations without further human intervention. The proposed approach will also develop cooperative learning strategies to share with extended skills to facilitate exploration and prevent agents from getting confused by the action details. In addition, this project will develop self-motivated learning structures to contribute towards the global objectives for team-wide success in a distributed perspective. The developed methods and associated architectures will provide critical insights and guidelines to foster autonomous learning and generalization in CPS. The integration of research and education plans will prepare the future workforce in the fields of CPS, artificial intelligence, learning and control. The outreach activities will build connections between the CPS research, and minority groups (women and Hispanic students), K-12, and college students through various learning approaches.This project is in response to the NSF CAREER 20-525 solicitation.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.