CITI seminar – François Michaud (Université de Sherbrooke) – 26/05 at 11:00

Speaker: François is Prof. at University of Sherbrooke (Canada), and leading the IntroLab at the 3IT institute.

Date: 26/05/2023

Time: 11h00

Place: Amphi Chappe/Lamarr, 6 avenue des arts, La Doua Campus

Title: Working Toward Human-Robot Symbiosis

Abstract: Human-robot symbiosis implies developing robotic systems that can collaborate with humans in open and ‘messy’ conditions, meaning unpredictable real-life settings, such as those found in assistive healthcare and work environments. Achieving human-robot symbiosis requires humanizing the sensing, perception, reasoning, and actuating capabilities based on evaluating human safety, well-being, acceptability, and usability. Researchers need to adopt a holistic approach enabling robots to seamlessly ‘see, hear and be’ in everyday settings, and design robots that are situationally balanced, in which complexity levels of sensory, motor, and artificial intelligence (AI)/cognitive capabilities are matched with the environment and people. This presentation addresses an overview of interactive robots and systems developed at IntRoLab, Université de Sherbrooke, involving compliant actuators, assistive robot platforms, telepresence robots, vision-based SLAM, drone intrusion, weed remoal robot, robot companion and robotic living labs.

Bio: François Michaud, Ph.D., is an engineer and full professor in the Department of Electrical and Computer Engineering at the Université de Sherbrooke, in Québec Canada. Holder of the Canada Research Chair in Mobile Robotics and Intelligent Autonomous Systems from 2001 to 2011, his research activities are aimed at integrating intelligent autonomous robotic systems into everyday operating conditions, to improve the well-being of people. His expertise is in human-robot interaction, assistive robotics, telepresence robotics, robot design and cognitive robotics. He has extensive experience in initiating and conducting interdisciplinary and intersectoral research projects involving collaborators in physiotherapy, occupational therapy, agriculture, child psychiatry, education, cognitive science, manufacturing, arts and automotive. He has published over 225 peer-reviewed papers in journals and international conferences (h-index 50), has been awarded 8 patents, has five significant distributed open source (software and hardware) contributions used by the robotics community, and has received funding over 50 M$ CAD supporting a broad range of research initiatives. He is the founding director of the Interdisciplinary Institute for Technological Innovation (3IT) (2008 – 2015), co-founder of Robotique FIRST Quebec (2010 – ), founder of Quebec Strategic Cluster INTER (Interactive Technologies in Rehabilitation Engineering) (2011 – ), and co-founder of a graduate training program CoRoM (COllaborative RObotics for Manufacturing). He is the Editor-in-Chief of Springer Nature Current Robotics Reports. He is also the founding director of the Bachelor of Robotics Engineering Program (2017 – ) at the Université de Sherbrooke, the first and only one in Canada.

PhD Defence: “Spatio-temporal Data Analysis for Dynamic Phenomenon Monitoring Using Mobile Sensors”, Ichrak Mokhtari, Amphi Chappe Building, 6th of June 2023 at 10 AM

The defense will take place on Tuesday 6th June at 10 AM in the Heidi Lamarr building (Amphi Chappe), Insa-Lyon, Villeurbanne.


Spatio-temporal Data Analysis for Dynamic Phenomenon Monitoring Using Mobile Sensors


Monitoring air pollution in emergencies (industrial accidents, terrorist attacks, volcanic eruptions, etc.) is of utmost importance given the dramatic effects that the released pollutants can cause on both human health and the environment. In these situations, the pollution plume is strongly dynamic leading to a fast dispersion of pollutants in the atmosphere. Thus, the need for real-time response is very strong and a solution to get a precise mapping of pollution dispersion is needed to mitigate risks.

This thesis focuses on the monitoring of air pollution in emergencies using a fleet of drones, with three main areas of investigation: 1) the spatiotemporal prediction of pollution plume evolution; 2) the optimal planning of drones trajectories to improve pollution mapping; and 3) the development of a generic solution for dynamic pollution monitoring. Through this work, we
propose a spatio-temporal Deep Learning model for multi-point forecasting of pollution concentrations, and we built upon several uncertainty quantification techniques to make it more trustworthy. Furthermore, we examine and identify the main challenges related to the underlying phenomena as well as its emergency context, and we suggest a new systemic approach for monitoring dynamic air pollution based on aerial sensing, that combines Deep Learning approaches, with Data Assimilation techniques, while relying at the same time on adequate path planning
strategies. The framework is then extended to address the data scarcity issues encountered in such situations through a transfer learning solution based on physical models. Finally, we meticulously address the drones’ path planning problem to improve the air pollution mapping quality, and we provide a Multi-Agent Reinforcement Learning solution.

Keywords: Monitoring Dynamic Air Pollution, Spatio-temporal Forecasting, Deep Learning, Multi-Agent Reinforcement Learning, Drones.


  • NATALIZIO, Enrico Professeur des universités TII, Abu Dhabi Rapporteur
  • MITTON, Natalie Directrice de recherche INRIA Rapportrice
  • GARCIA, christophe Professeur des universités INSA-LYON Examinateur
  • CARNEIRO Viana, Aline Directrice de recherche INRIA Examinatrice
  • LABENTALl, Bérengère Directrice de recherche Université Gustave Eiffel Examinatrice
  • RIVANO, hervé Professeur des universités INSA-LYON Directeur de thèse
  • BECHKIT, Walid Maître de conférences INSA-LYON Co-directeur de thèse