2019-2020 / INFO0948-2

Introduction to intelligent robotics


30h Th, 4h Pr, 80h Proj.

Number of credits

 Master in data science (120 ECTS)5 crédits 
 Master in electrical engineering (120 ECTS)5 crédits 
 Master of science in computer science and engineering (120 ECTS)5 crédits 
 Master of science in computer science and engineering (120 ECTS) (double diplômation avec HEC)5 crédits 
 Master in data science and engineering (120 ECTS)5 crédits 
 Master in computer science (120 ECTS)5 crédits 
 Master in computer science (120 ECTS) (double diplômation avec HEC)5 crédits 
 Master in mechanical engineering (120 ECTS)5 crédits 


Pierre Sacré

Language(s) of instruction

English language

Organisation and examination

Teaching in the second semester


Schedule online

Units courses prerequisite and corequisite

Prerequisite or corequisite units are presented within each program

Learning unit contents

1: Basics: SE(3) geometry, sensors, actuators, controllers, kinematics. 2: Mobile robots: Locomotion, localization, navigation, SLAM. 3: Arms and grippers: Reaching, grasping, grasp learning. 4: Computer Vision: Feature extraction (Edge, Harris), Fitting (Ransac, Hough), Tracking (Kalman, Nonparametric), Object recognition (PCA, probabilistic model). See http://renaud-detry.net/teaching/info0948/

Learning outcomes of the learning unit

At the end of the course, students will be able to:

  • Extract information from video streams (identity/position of objects/persons, body pose, 3D structure).
  • Plan actions from sensory data (navigation, grasping, via optimization, learning or control).
  • Translate these actions into a sequence of motor commands that can be executed on the robot.
A group project will allow students to practice the concepts studied in class. Students will program a robotic agent capable of processing images, plan actions, and execute these actions on a robot. The agent will be evaluated in a robot simulator (V-REP).

Prerequisite knowledge and skills

  • Programming skills
  • Basic math
  • Elements of probabilities and statistics

Planned learning activities and teaching methods

  • Oral Courses
  • Seminars
  • Group project

Mode of delivery (face-to-face ; distance-learning)

Face-to-face delivery.

Recommended or required readings

The course is largely based on the book Robotics, Vision and Control: Fundamental Algorithms in MATLAB, written by Peter Corke, published by Springer in 2011. See the course page for download and purchase information.

Assessment methods and criteria

Group project (evaluating the assimilation of practical notions).

Work placement(s)

Organizational remarks

Course webpage: http://renaud-detry.net/teaching/info0948/


Renaud Detry: http://renaud-detry.net