Modern intelligent embodied agents, such as service robots and autonomous vehicles, interact frequently with humans in dynamic, uncertain environments. They may also collaborate as a team through effective communication and coordination to enhance task success, safety, and efficiency. These bring a few significant challenges. First, building reliable agents that safely navigate multi-agent scenarios requires scalable and generalizable prediction of surrounding agents' behaviors and robust decision making under environmental uncertainty, especially in out-of-distribution (OOD) scenarios. Second, effective cooperation between agents requires efficient communication and information fusion strategies and reliable task allocation and planning for complex long-horizon tasks. In this talk, I will introduce a series of our recent work that addresses these challenges to enable safe and trustworthy embodied agents and their application to autonomous driving and service robots. Specifically, I will first demonstrate principled uncertainty quantification techniques and how they enable generalizable prediction and planning in out-of-distribution scenarios. Then, I will talk about effective approaches to enable efficient multi-agent communication and cooperation in centralized and decentralized settings.
Dr. Jiachen Li is an Assistant Professor in the Department of Electrical and Computer Engineering (ECE) and a cooperating faculty member in the Department of Computer Science and Engineering (CSE) at the University of California, Riverside. He leads the Trustworthy Autonomous Systems Laboratory (TASL) and is affiliated with the Riverside Artificial Intelligence Research Institute (RAISE), the Center for Robotics and Intelligent Systems (CRIS), and the Center for Environmental Research and Technology (CE-CERT). Before joining UCR, he was a postdoctoral scholar at Stanford University and earned his Ph.D. from the University of California, Berkeley. Dr. Li was recognized as an RSS Robotics Pioneer in 2022 and named an ASME Rising Star in 2023. He serves as an associate editor or reviewer for over thirty leading journals and conferences and has organized multiple workshops on robotics, machine learning, computer vision, and intelligent transportation systems at top conferences. His research interests span robotics, trustworthy AI & ML, reinforcement learning, control, optimization, and computer vision, with applications to intelligent autonomous systems (e.g., service robots, autonomous vehicles, manipulators, and cyber-physical systems), particularly in human-robot interaction and multi-agent systems.