MAE Seminar: Uncertainty-Aware Intelligence for Autonomous Space Systems

Details

MAE Seminar: Uncertainty-Aware Intelligence for Autonomous Space Systems
Date: February 26, 2026
Time: 12:50 PM Location: MAE-A 303

Dr. Dr. Xiaoli Bai
Associate Professor
Department of Mechanical and Aerospace Engineering
Rutgers, The State University of New Jersey

Abstract
Autonomous space systems increasingly operate in environments characterized by incomplete sensing, uncertain models, limited communication, and complex interactions with other agents. These challenges make it difficult for autonomous systems to rely on fixed margins or nominal assumptions, and instead require principled ways to represent, propagate, and reason about uncertainty. This seminar presents a research program on uncertainty-aware intelligence for autonomous space systems, integrating physics-based modeling, learning, and uncertainty quantification to support reliable autonomy under uncertainty.

Representative applications span multiple space autonomy domains, including Physics–Machine Learning orbit prediction for Space Situational Awareness, estimation of unknown and non-cooperative space objects during proximity operations, robust online optimal trajectory planning for space manipulators, and physics-informed probabilistic thermospheric density prediction. Across these problems, uncertainty arises from sensing limitations, environmental variability, and model mismatch, and directly impacts autonomy performance and decision-making.
The seminar highlights common principles for integrating uncertainty into learning-enabled autonomy. Through examples, the talk illustrates how uncertainty-aware approaches can reduce false alarms, improve robustness, and enable more scalable and trustworthy autonomous space systems operating in challenging real-world environments.

Biography
Dr. Xiaoli Bai is an Associate Professor in the Department of Mechanical and Aerospace Engineering at Rutgers, The State University of New Jersey. She received her Ph.D. in Aerospace Engineering from Texas A&M University. Her research spans astrodynamics and space situational awareness, spacecraft guidance and control, space robotics, and the prediction of thermospheric density. Dr. Bai was elected an Associate Fellow of the American Institute of Aeronautics and Astronautics (AIAA) for the Class of 2021. Her honors include the 2019 NASA Early Career Faculty Award, the 2016 Air Force Office of Scientific Research Young Investigator Research Program Award, the 2018 Outstanding Young Aerospace Engineer Award from Texas A&M University, the 2018 A. Walter Tyson Assistant Professor Award from Rutgers, and the Amelia Earhart Fellowship.

Faculty Host: Dr. John Conklin