Mengxue Hou

Mengxue Hou

Assistant Professor, Electrical Engineering

University of Notre Dame


I received B.S. degree in Electrical Engineering from Shanghai Jiao Tong University, Shanghai, China, in 2016, and PhD in Electrical and Computer Engineering from Georgia Institute of Technology, Atlanta, GA, USA in 2022. From Aug. 2022 to Jun. 2023, I was a Lillian Gilbreth Postdoc Fellow, in College of Engineering, Purdue University, West Lafayette, USA. My research interests include robotics, autonomy, and human robot collaboration. I aim to devise practical, computationally-efficient, and provably-correct algorithms that prepare robotic systems to be cognizant, taskable, and adaptive, and can collaborate with humans to co-exist in a complex, ever-changing and unknown environment.

P.S. Pronounciation of my first name is “mung-shway”. You can also call me Meng.

Prospective Students & Postdoc: I am actively looking for talented graduate/undergrad students & postdoc fellows interested in robotics, control & learning. Please visit our lab for more information!

  • Robotics
  • Autonomy
  • Machine Learning
  • Human Robot Teaming
  • PhD in Electrical and Computer Engineering, 2022

    Georgia Institute of Technology, Atlanta, GA, US

  • BS in Electrical Engineering, 2016

    Shanghai Jiao Tong University, Shanghai, China

Research Experience

College of Engineering, Purdue University
Lillian Gilbreth Postdoc Fellow
Aug 2022 – Jun 2023 West Lafayette, IN

Advisors: Prof. Shaoshuai Mou, Prof. Shreyas Sundaram

  • Human Robot Collaboration
  • Multi-agent Systems
Electrical and Computer Engineering, Georgia Tech
Research Assistant
Aug 2016 – Aug 2022 Atlanta, GA

Advisor: Prof. Fumin Zhang

  • Marine Autonomy
  • Task and Motion Planning
  • Nonlinear Filtering

Recent News

All news»

[08/23] Welcome Xiaoran and Yu to ROAR Lab!

[08/23] I have started as assistant professor at University of Notre Dame!

[04/23] I am selected to attend the CPS Rising Stars 2023 workshop at University of Virginia!

[04/23] I am co-organizing an invited session on Predictive Control and Planning Methods for Robotics Systems at ACC 2023!


To enable exploration of unstructured and dynamic open worlds, robotic systems have to collaborate with human operators to co-exist in a complex, ever-changing and unknown environment, and should feature behaviors that are cognizant, adaptive, and taskable: the robots need to be aware of their capabilities, identify the changes in environmental dynamics, learn from past experiences to improve system performance, and understand high-level instructions to plan multi-modal strategies that are dependent on the context in which the system is operating. Such features result in the following research questions:

Cognizant: how to represent the agent’s knowledge in an unstructured environment, without a pre-defined set of scene parameters?

Taskable: how to efficiently discover useful multi-modal distributed strategies for human-robot teams?

Adaptive: how to learn from past sensory data to build skills that can adapt over time to the particularities of the environment?

In this context, my research focuses on foundational advances in robotics and autonomy. I aim to devise practical, computationally-efficient, and provably-correct algorithms that prepare autonomous systems working synergistically with human operators to explore unknown, unstructured and dynamic environments. I will also seek to develop robotic platforms to validate the autonomy algorithms. The underlying hypothesis in my research is that the interactions between agents and the environment provide rich information: on one hand, the robot can leverage its actions and observed effects to train a high-fidelity prediction model (Thrust A). The learned model enables planning and control synergies of interaction policy for human-robot team (Thrust B). On the other hand, the robot can also use the historical perception data to directly learn the skills to achieve efficient sensorimotor understanding and planning (Thrust C).


Quickly discover relevant content by filtering publications.
(2023). Dynamic Event-triggered Integrated Task and Motion Planning for Process-aware Source Seeking. Autonomous Robots (under review).


(2023). OceanChat: Piloting Autonomous Underwater Vehicles in Natural Language. In ICRA (under review).

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(2023). Laplacian Regularized Motion Tomography for Underwater Vehicle Flow Mapping with Sporadic Localization Measurements. Autonomous Robots (under review).

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(2023). Mori-Zwanzig Approach for Belief Abstraction with Application to Belief Space Planning. Autonomous Robots (under review).

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(2023). Integrated Task and Motion Planning for Process-aware Source Seeking. In ACC.

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(2023). Real-time Autonomous Glider Navigation Software. OCEANS (accepted).


(2023). Anomaly Detection of Underwater Gliders Verified by Deployment Data. Underwater Technology.


(2023). An Interleaved Algorithm for Integration of Robotic Task and Motion Planning. In ACC.

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(2023). High-dimensional Optimal Density Control with Wasserstein Metric Matching. In CDC (accepted).

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(2022). Bounded Cost Path Planning for Underwater Vehicles Assisted by a Time-Invariant Partitioned Flow Field Model. Frontiers in Robotics and AI (8), 575267.

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(2022). Human Pointing Motion during Interaction with an Autonomous Blimp. Scientific Reports (12), 11402.

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(2022). Method of Evolving Junction on Optimal Path Planning in Flows Fields. Autonomous Robots.


(2021). The Rational Selection of Goal Operations and the Integration of Search Strategies with Goal-driven Marine Autonomy. ACS.


(2021). Improved trajectory tracing of underwater vehicles for flow field mapping. International Journal of Intelligent Robotics and Applications.

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(2021). Belief Space Partitioning for Symbolic Motion Planning. In ICRA.

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(2020). Bounded Cost HTN Planning for Marine Autonomy. In OCEANS.

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(2019). An LSTM based Kalman Filter for Spatio-temporal Ocean Currents Assimilation. In WUWNet.


(2019). Modeling Pointing Tasks in Human-Blimp Interactions. In CCTA.

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(2019). Partitioning Ocean Flow Field for Underwater Vehicle Path Planning. In OCEANS.

PDF Cite Poster DOI

(2018). Parameter Identification of Blimp Dynamics through Swinging Motion. ICARCV.



Please visit ROAR (Robotics and Autonomy Research) lab website for more information. icon


Teaching at University at Notre Dame

EE 67074-AI Planning: from Graph Search to Reinforcement Learning, Fall 2023

EE 20221-Signal and Information Systems, Spring 2024

Teaching at Georgia Institute of Technology

ECE Vertically Integrated Projects (VIP), Course Instructor, Fall 2018 - Spring 2019

ECE 2026, Introduction to Signal Processing, Teaching Assistant, Fall 2016 - Summer 2017