Research Projects

Research Areas

• Computational Intelligence: Reinforcement Learning, Adaptive Dynamic Programming

• Cyber-Physical Systems: Networked Control Systems, Event-Triggered Control Systems

• Intelligent Control: Adaptive Control, Learning-Based Control

• Robotics: Adaptive Learning, Path Planning, Maze Navigation, Multi-Agent Cooperation Systems

Selected Research Projects

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

• National Science Foundation (NSF), Award CNS #2047010, $503,597, 2021-2026.

Intellectual Merit: The goal of this project is to advance foundational knowledge and scientific methodologies of reinforcement learning for generalization and scalability in cyber-physical systems (CPS). Motivated by the recent research in neurobiology and psychology, this project designs 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 and reduce communication cost needed for proper collaboration. 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 proposed approach will facilitate learning in high-dimensional and heterogeneous environment. Meanwhile, the work will be validated on a developed heterogeneous networked robots testbed to provide new solutions in the critical CPS application area. The developed methods and associated architectures will provide critical insights and guidelines to foster autonomous learning and generalization in CPS.

Broader Impacts: This research will advance the scientific foundations and methodologies of intelligent control design for CPS in high-dimensional and heterogeneous environment. The developed algorithms and associated architectures will provide critical insights and guidelines to foster autonomous learning and generalization towards human-level intelligence in CPS. This project will also fulfill a critical need in the community by cultivating and preparing future workforce in the field of CPS, AI and control. The outreach and education activities will leverage the resource for women, Hispanic, and minorities by collaborating with FAU SWE section and regional community. The integration of research and education plans will prepare the future workforce in the fields of CPS, artificial intelligence, learning and control.

SCH: Enabling Disabled People to Control the Full Dexterity of Wearable Assistive Robots (Co-PI)

• National Science Foundation (NSF), Award IIS #2205205, $1,199,998, 2022-2026. PI: Dr. Erik Engeberg

This Smart Health (SCH) award will contribute to the advancement of the national health and welfare by developing technology to help the millions of people who have suffered an amputation or who have a congenital deficiency of an upper limb. These problems create a devastating impact on the quality of the victims’ lives and pose a significant financial burden to people in the U.S.A. Unfortunately, the state of the art in prosthetic hand replacements is not comparable to their popular portrayal in sci-fi movies, leaving upper limb-absent people with rudimentary control of basic grasp functions. This work will explore methods to help upper limb-absent people learn advanced control of sophisticated prosthetic hands with an automated training regimen that can be used at home. Automating this aspect of healthcare with remote learning functionality can help disabled people access treatment more quickly and at a lower cost. Furthermore, research from this grant will be used to create learning experiences for high school students from low-income households to help educate the next generation of engineers and scientists. Thus, this research can benefit the society and economy of the USA.

MRI: Development of a mmWave-Networked Robotic Testbed for Multi-Agent AI Learning and Operations (Co-PI)

• National Science Foundation (NSF), Award CNS #2117822, $1,000,000, 2021-2025. PI: Dr. Dimitris Pados

This project aims to develop an experimental shared re-programmable platform that will provide robotics and research communities with an instrument that can be a catalyst in the field. The platform will advance research activities in the field of multi-agent AI, mmWave networking and communications. It will enable rapid testing and repeatable comparable evaluation of collaborative swarming operations and distributed sensing, positioning, timing, navigation, and communication developed by researchers at different institutions. To the best of the investigators’ knowledge) to recreate mobility and dynamism seen in real world scenarios it will be the first to allow thorough experimental evaluation of multifunction mmWave radios, and creates an opportunity to strengthen the on-going partnerships between researchers in mmWave networking and an industrial player that has been leading the development of mmWave and subTHz software-defined radios.

The platform offers a unique research and training opportunity for undergraduate and graduate students at Florida Atlantic University (FAU) for the potential to train information technology professionals and scientists with unique theoretical and system design skills in mmWave wireless networking, robotics, and multiple-agent AI operations. Indeed, it has the potential to become the experimental platform of choice for mmWave communications and networking and robotics research community. Moreover, researchers at other institutions will be allowed (and trained appropriately) to use the platform.

Collaborative Research: Autonomous Hierarchical Adaptive Dynamic Programming for Decision Making in Complex Environment (PI)

• National Science Foundation (NSF), Award ECCS #1947419, $253,297, 2019-2022.

The recent big wave of artificial intelligence (AI) not only provided tremendous advancements ranging from fundamental research to a wide range of exciting applications, but also presents enormous amounts of opportunities as well as challenges to the community. Among many of the AI techniques, adaptive dynamic programming and reinforcement learning (ADP/RL) is widely considered as one of the key methodologies for learning-based intelligent decision-making process.

The objective of this project is to develop an innovative autonomous hierarchical ADP/RL approach for decision making in complex environments. By autonomously providing a hierarchical representation of sub-goals for improved learning and exploration capability, the proposed research provides a new approach to systematically and adaptively develop an optimal multi-step hierarchical temporal abstraction sequence, rather than the one-step primitive action in traditional methods. The research method advances the foundations, principles, architectures, and algorithms for autonomous learning and hierarchical control, which will facilitate the capability of learning and generalization for decision-making. This project provides unique opportunities to attract and educate future professionals by bridging the connections of ADP/RL and energy systems, and for students to work on cutting-edge problems. The team consists of two PIs with strong collaborations and complementary expertise in computational intelligence, machine learning, autonomous control, and the smart grid.

CRII: CPS: A Self-Learning Intelligent Control Framework for Networked Cyber-Physical Systems (PI)

• • National Science Foundation (NSF), Award CNS #1947418, $174,365, 2019-2022.