Mengxue Hou received the PhD degree from Electrical and Computer Engineering at Georgia Institute of Technology, Atlanta, GA, USA in 2022, and B.S. degree from Electrical Engineering at Shanghai Jiao Tong University, Shanghai, China, in 2016. Since 2022, she is working as the Lillian Gilbreth Postdoctoral Fellow at College of Engineering, Purdue University. Her research interests include robotics, mobile sensor networks, and shared autonomy.
To enable a smart and autonomous system to be cognizant, taskable, and adaptive in exploring an unknown and unstructured environment, robotic decision-making relies on learning a parameterized knowledge representation. However, one fundamental challenge in deriving the parameterized representation is the undesirable trade-off between computation efficiency and model fidelity. This talk addresses this challenge in the context of underwater vehicle navigation in unknown marine environments. To improve fidelity of the reduced-order model, we develop a learning method to generate a non-Markovian reduced-order representation of the environmental dynamics. By incorporating the Mori-Zwanzig formalism, we prove that the proposed learning-based abstraction achieves a time-uniform model reduction error bound. Further, taking advantage of the abstracted model, we develop a novel hierarchical planning algorithm to generate the optimal multi-modal strategies with low computation cost.