Hanqing ZHAO

Please refer to the CV page for contact details.

Hanqing Zhao (he/him/il) is a Postdoctoral Researcher at the MIST Lab of École Polytechnique de Montréal, supervised by Giovanni Beltrame.

He will be joining Université Laval as a Professeur adjoint (Assistant Professor) in the Département de génie électrique et de génie informatique in September 2025, where he will be part of the Laboratoire de Vision et Systèmes Numériques (LVSN).

Hanqing began his academic journey at the École Centrale de Pékin (Université Beihang). He earned Ingénieur civil en informatique degree from École Polytechnique de Bruxelles (Université libre de Bruxelles), supervised by Marco Dorigo. He interned at Nokia Bell Labs Antwerp with Jean-François Macq and Christoph Stevens. He received Ph.D. in Computer Science (robotics) from McGill University, supervised by Gregory Dudek and Xue Liu.

His research focuses on enabling robots to accomplish complex tasks while remaining resilient to faults and external disturbances. He leverages machine learning, control theory, and advanced consensus achievement techniques, such as (inverse) reinforcement learning, supervised learning, Blockchain technologies and Control Barrier Functions to develop robust, (especially multi-)robot systems.

news

Jul 01, 2025	I will be joining Université Laval as a Professeur adjoint (Assistant Professor) in September 2025.
Oct 01, 2024	I joined MIST Lab at Polytechnique Montréal as a Postdoctoral Researcher with Giovanni Beltrame, working on multi-robot planning in collaboration with Quaze Technologies.
Jul 09, 2024	I defended my Ph.D. dissertation, titled Towards Secure Robot Intelligence through Rewards-based Fault Management, at McGill University.

selected publications

2023

IROS

A Generic Framework for Byzantine-tolerant Consensus Achievement in Robot Swarms

Hanqing Zhao, Alexandre Pacheco, Volker Strobel, and 4 more authors

In IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS), 2023

Bib PDF Video

@inproceedings{zhao2023generic,
  title = {A Generic Framework for Byzantine-tolerant Consensus Achievement in Robot Swarms},
  author = {Zhao, Hanqing and Pacheco, Alexandre and Strobel, Volker and Reina, Andreagiovanni and Liu, Xue and Dudek, Gregory and Dorigo, Marco},
  booktitle = {IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS)},
  pages = {8839--8846},
  year = {2023},
  organization = {IEEE},
}

IROS

Zero-Shot Fault Detection for Manipulators Through Bayesian Inverse Reinforcement Learning

Hanqing Zhao, Xue Liu, and Gregory Dudek

In IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS), 2023

Bib PDF

@inproceedings{zhao2023zero,
  title = {Zero-Shot Fault Detection for Manipulators Through Bayesian Inverse Reinforcement Learning},
  author = {Zhao, Hanqing and Liu, Xue and Dudek, Gregory},
  booktitle = {IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS)},
  pages = {3582--3589},
  year = {2023},
  organization = {IEEE},
}

2022

IROS

Behaviour Learning with Adaptive Motif Discovery and Interacting Multiple Model

Hanqing Zhao, Travis Manderson, Hao Zhang, and 2 more authors

In IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS), 2022

Bib PDF

@inproceedings{zhao2022behaviour,
  title = {Behaviour Learning with Adaptive Motif Discovery and Interacting Multiple Model},
  author = {Zhao, Hanqing and Manderson, Travis and Zhang, Hao and Liu, Xue and Dudek, Gregory},
  booktitle = {IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS)},
  pages = {10788--10794},
  year = {2022},
  organization = {IEEE},
}

2021

MMAR

General Dynamic Neural Networks for the Adaptive Tuning of an Omni-Directional Drive System for Reactive Swarm Robotics

Hanqing Zhao, Marco Dorigo, and Michael Allwright

In International Conference on Methods and Models in Automation and Robotics (MMAR), 2021

Bib PDF Code

@inproceedings{zhao2021general,
  title = {General Dynamic Neural Networks for the Adaptive Tuning of an Omni-Directional Drive System for Reactive Swarm Robotics},
  author = {Zhao, Hanqing and Dorigo, Marco and Allwright, Michael},
  booktitle = {International Conference on Methods and Models in Automation and Robotics (MMAR)},
  pages = {79--84},
  year = {2021},
  organization = {IEEE},
}