SE- Machine learning in robotics

Machine Learning in Robotics

Course Description

In this seminar, current topics and methods of machine learning with application in robotics are discussed. In particular, we focus on new developments in deep learning applications – namely robot vision, cognitive-developmental tasks, and multimodal learning– in humanoid robotics. To pass this course, the students should develop machine learning models with robot datasets, propose team projects, presents results in oral presentations and written reports.

Update: According to HU's summer semester policy and due to Covid-19, the lectures, assignments, and presentations will be held online via Zoom.

Prerequisites

Some experience with machine learning, programming, and robotics is required. Although we will not assume that you have an extensive background in these fields, basic understanding below-listed items will be necessary to follow the content of the course.

Linear Algebra: Throughout the course, we will intensely perform Matrix and vector operations to construct our machine learning pipelines. You should be familiar with the basics and notations of matrix/vector calculus.
Probability and Statistics: You should have (at least) a sound knowledge of the basic probability and statistics concepts: random variable, probability distribution, conditional probability, mean, variance, etc.
Programming: The programming language of this course is Python 3. If you are comfortable with other programming languages (e.g., C++/Matlab), the tutorials will help you to code in Python.
Tools/libraries: You should be familiar with software development tools and libraries: Git, TensorFlow, Keras, Numpy, Matplotlib (or Seaborn), etc.

During the first weeks of the course, we will point out supplementary resources to introduce the bare minimum requirements of the course. Additionally, we will arrange tutorial sessions on machine learning engineering to introduce common tools and practices to develop machine learnign pipelines and deploy the machine learning models into simulated robot platforms.

Objectives

Upon completion of this seminar course, students will be able to:

develop hands-on programming skills to build multimodal machine learning pipeline
understand state-of-the-art machine learning algorithms: perceptron, K-Means, self organizing maps, Q-learnig, and deep neural networks
grasp the working principles of different sensors: color and depth cameras, range sensors such as sonar and infrared, laser scanner, etc.
deploy your machine learning models on actual (and virtual) robots

Instructors

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