Lecture 2 - Learning

Abstract

This lecture presents techniques to gather data which can then be used to train a robot’s controller, as well as the caveats of these methods. Two popular approaches are Learning from Demonstrations, where an expert provides examples for the desired task, and Reinforcement Learning, where the robot learns through trial and error by exploring the task space. Optimal Control can also be used to generate large sets of feasible trajectories. Each method has different requirements and the choice of which method to use depends on factors such as the desired task, the prior knowledge of the robot and environment, the trainer and the time available

Lecture Video

Slides

Click here to download a pdf version of the ppt presentation.

Exercises Instructions

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Code for MATLAB Exercises

The recommended way to do the MATLAB exercises is to download the entire repository once, then go to each lecture's folder. Detailled instructions for installation can be found on the Software page.

Click here to download the corresponding exercise for this lecture as a zip file.
Note you will also need this libraries folder placed with the correct directory structure.

Robotic Implementation