MICRO-462 / 4 credits

Teacher: Billard Aude

Language: English


Summary

To cope with constant and unexpected changes in their environment, robots need to adapt their paths rapidly and appropriately without endangering humans. this course presents method to react within millisecunds.

Content

Keywords

Robotics, Machine Learning, Nonlinear Control, Dynamical systems theory, Robust and Adaptive Path Planning for Manipulation and Navigation

Learning Prerequisites

Required courses

MICRO-452 Basics of Mobile Robotics

MICRO-455 - Applied Machine Learning

Recommended courses

COM-502 Dynamical system theory for engineers

ME-225 Dynamical systems

MATH-325 Dynamical systems and bifurcation

Important concepts to start the course

Nonlinear regression techniques and classification from machine learning: GMM/GMR, SVM/SVR, NN

Differential equation, fixed points analysis, theory of stability in linear and nonlinear dynamical systems

Learning Outcomes

By the end of the course, the student must be able to:

  • Choose for learning robotic tasks
  • Formalize stability of a learned controller
  • Prove stability of a learned controller
  • Transpose example of robotic application to another application

Transversal skills

  • Use a work methodology appropriate to the task.

Teaching methods

The course will be composed of lectures and of exercises and practice sessions. The class will be a flipped-class format with the theory presented for 45 minutes in sets of short videos. 45 minutes of interactive lecture in class will revisit the theoretical concepts with interactive exercises. In the exercise sessions, students will get to solve equations on paper but also to program the algorithms. Programming language will be matlab, but students who want can use python.

Expected student activities

Students are expected to watch the videos prior to the interactive lecture, as the interactive lecture will not repeat the video but go in more depth in the concepts presented in the videos.

Students are expected to attend the exercise sessions and the computer-based practice sessions. They should revise the class notes prior to going to practical session to be on top of the the theoretical material prior to applying it.

Students who are no longer up to date with the pre-requisites should work on these in parralel to taking the class.

Assessment methods

100% of the grade is based on an oral exam.

Supervision

Office hours No
Assistants Yes
Forum Yes

Resources

Virtual desktop infrastructure (VDI)

No

Notes/Handbook

MIT Press Book “Learning for Adaptive and Reactive Robot Control”

https://mitpress.mit.edu/books/learning-adaptive-and-reactive-robot-control

Websites

Moodle Link

Videos

In the programs

  • Semester: Spring
  • Exam form: During the semester (summer session)
  • Subject examined: Learning and adaptative control for robots
  • Lecture: 2 Hour(s) per week x 14 weeks
  • Exercises: 2 Hour(s) per week x 14 weeks
  • Semester: Spring
  • Exam form: During the semester (summer session)
  • Subject examined: Learning and adaptative control for robots
  • Lecture: 2 Hour(s) per week x 14 weeks
  • Exercises: 2 Hour(s) per week x 14 weeks

Reference week

 MoTuWeThFr
8-9     
9-10     
10-11     
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