Machine LearningModelling


  • Undegraduate-level linear algebra.
  • Tutorial (TP) sessions will be in Python. Basic knowledge of the language and of general programming concepts (functions, loops, recursivity, data structures, …) are expected.
  • It is recommended to take the « Convex optimization » course by A. d’Aspremont at the same time.

Objectif du cours

A large part of the recent progress in robotics has sided with advances in machine learning, optimization and computer vision. The objective of this lecture is to introduce the general conceptual tools behind these advances and show how they have enabled robots to perceive the world and perform tasks ranging, beyond factory automation, to highly-dynamic saltos or mountain hikes. The course covers modeling and simulation of robotic systems, motion planning, inverse problems for motion control, optimal control, and reinforcement learning. It also includes practical exercises with state-of-the-art robotics libraries, and a broader reflection on our responsibilities when it comes to doing research and innovation in robotics.

Plus d’informations : cliquez ici

Organisation des séances

8 courses of 3h + final exam (4 h)

  1. Introduction: 3h lecture
  2. Motion planning for robotics: 2h lecture, 1h TP
  3. Inverse problems for robot control: 2h lecture, 1h TP
  4. Optimal control and trajectory optimization: 2h lecture, 1h TP
  5. Perception and estimation: 3h lecture
  6. Reinforcement learning for robotics: 2h lecture, 1h TP
  7. Legged locomotion: 2h lecture, 1h TP
  8. Responsible robotics: 3h lecture
  9. Final exam: 4h student seminar (Project/report presentations)

Mode de validation

weekly homework (20%) + project or an article study (80%)



  • A mathematical introduction to robotic manipulation. Murray, R. M., Li, Z., & Sastry, S. S. (2017). CRC press.
  • Calculus of variations and optimal control theory: a concise introduction. Liberzon, D. (2011). Princeton university press.
  • State Estimation for Robotiucs. Barfoot, T. (2017). Cambridge University Press.
  • Introduction to humanoid robotics. Kajita, S., Hirukawa, H., Harada, K., & Yokoi, K. (2014). Springer.

Thèmes abordés

  • modeling and simulation of robotic systems
  • motion planning
  • inverse problems
  • optimal control
  • perception and estimation
  • reinforcement learning
  • legged locomotion
Les intervenants


(Mines Paris)

Stéphane CARON




Pierre Brice WIEBER


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