Teaching
Present
Planification de trajectoires
M2 AVR, Univ. Lorraine (2018—)
This lecture introduces some path-planning techniques. It is part of the Modélisation de l’environnement et planification de trajectoires lecture in the computer science master AVR.
Material (in French):
- lecture slides
- exercises and exam
- 2018—2019: exercise, exercise, exam and solution
- 2019—2020: exercise, exam and solution
Autonomous Robotics
ST5, CentraleSupélec Metz (2019—)
It is an introductory lecture on autonomous robotics with experiments in simulation and real robot platforms. The target audience is second year student of an engineering school with good bases in computer science (M1-level).
The material (lecture slides, exercises and sample code) are available on the automatically generated course web page.
Methodological integration
M2 AVR, Univ. Lorraine (2018—)
This is a 3-months project on the Pepper robot. The project consists in designing and developing an application involving navigation and human-robot interaction aspects on the robot. The aim is to tie together knowledge gathered from several of the master’s lectures.
Various
ROS
Short presentation of main concepts of the ROS middleware (intended for master students but no strong requirement).
Material:
git
Short presentation of the git version control system.
Material:
Past
Introduction to autonomous robotics
3A SIR, CentraleSupélec Metz (2016, 2017, 2018, 2019)
Introductory lecture on autonomous mobile robotics. The targeted audience was last-year students in Systèmes Interactifs et Robotiques major at the CentraleSupélec engineering school in Metz.
Material (in French):
- Lecture slides
- exams
Information Processing for Robotics
ETH Zurich (2010, 2011)
Basic lectures on machine learning methods with a focus on robotics. Targeted audience was master students in mechatronics.
Material:
- Introduction to Learning and Probabilistic Reasoning
- Graphical models and Hidden Markov Models
- Online estimation: application to localization and mapping
- Regression
- Gaussian Processes
- Support Vector Machines
- Iterative Closest Point Algorithm