Interactive Machine Learning - IML

2022



Course Description


Interactive Machine Learning (IML) merges machine learning and human-computer interaction. While traditional machine learning systems process the data that have been given to them in advance, this course considers that the learning process could benefit from interactions with the environment as well as with a human, and that inputs and outputs from and for humans carry meaningful information. Indeed humans may provide input to a learning algorithm, including inputs in the form of labels, demonstrations, advice, rewards or rankings. The interaction is all the more useful as the human can guide along the learning process while adapting his guidance to the outputs of the algorithm. This interaction can be in the form of feedforward or feedback information. The timing of these interactions can be preset, left to the teacher’s initiative or even to the learner’s initiative. In the latter case, the algorithm called “active learner" can decide when, about what, how and with whom to interact to optimise its learning process. Thus a bidirectional dialogue can emerge.

Application will focus on robot programming covering topics including sensing in real-world environments, mapping, navigation, localization, kinematics and vision. Students will program virtual and physical robots interacting with the world using the Robot Operating System (ROS 2). Students will gain experience facing and overcoming the challenges of programming robots (e.g., sensor noise, edge cases due to environment variability, physical constraints of the robot and environment).


Course Info




Course Project



The project gives students the opportunity to implement a solution using interactive machine learning, means human in the loop, and to continue to grow their skills in robot programming.

Teaming & Logistics

You are expected to work with 1 other student for this project. A team of 3 will only be allowed if there is an odd number of students. Your team will submit your code and writeup together (in 1 Github repo).

If you are looking for a partner, send a Discord message.

Deliverables

You'll submit this project on Github

Please put your implementation plan within your README.md file. Your implementation plan should contain the following:

Writeup

Modify the README.md file as your writeup for this project. Please add pictures, Youtube videos, and/or embedded animated gifs to showcase and describe your work. Your writeup should contain:

Subject

Subject is free, it should contain:

Plagiarism

Tools to check code will be use to make sure students provide an original contribution. Cheating will outcome with 0/20 on IML course.

Deadline

Link to the github project should be sent by 15/01/2023



Grading