Introduction to Vision and Robotics

In 2015/2016 The course will be taught in Semester 2 starting 11 Jan 2015

Robotics and Vision applies AI techniques to the problem of making devices capable of interacting with the physical world. This includes moving around in the world (mobile robotics), moving things in the world (manipulation robotics), acquiring information by direct sensing of the world (e.g. machine vision) and, importantly, closing the loop by using sensing to control movement. This module introduces the basic concepts and methods in these areas and serves as an introduction to the more advanced robotics and vision modules.

Course Lecturers: 

Michael Herrmann Maurice Fallon
Email: michael DOT herrmann AT ed DOT ac DOT uk Email: maurice DOT fallon AT ed DOT ac DOT uk
Office: 1.26 Informatics Forum Office: 1.42 Informatics Forum
Phone: 651 3441 Phone: 651 7177

Other Personnel:

Demonstrators: Davide Modolo (d.modolo AT sms DOT ed DOT ac DOT uk) and Calum Imrie (s1120916 AT sms DOT ed DOT ac DOT uk)
Support of Distance Learning Version of Course: Bob Fisher

Schedule for Resident 3rd year Students:

The master schedule can be found in the 3rd year course descriptor.
Review Lectures: Twice per week, Mondays and Thursdays at 11.10-12.00. Appleton Tower, Lecture Theatre 3.
Supervised Lab Session: From week 2, Monday (14.10-15.00), Wednesday (12.10-14.00), Thursday (14:10 to 16:00). Forest Hills Robot teaching lab (G.A11).

Schedule for Distance Learning MSc Students:

This course will be simultaneously delivered to resident 3rd year informatics undergraduates and distance learning MSc students. Lecture videos are available online (see below). The review lectures will be streamed via the LEARN system allowing distrance students to ask questions (using 'Collaborate', TBC). The assignments will follow the same schedule and approach as for the local course. Distance students are required to purchase an Lego EVO kit and a student matlab license for the assignments. Assignments will be remotely assessed at a convenient time for our demonstrators and the student.

Inverted Classrom:

IMPORTANT INFORMATION: This course is not taught by the traditional lectures. Instead, IVR uses an Inverted Classroom method. This means that you will have about 15 hours of video to watch in your own time ('Lecture Videos'). This material is assessible. There are still 2 full class meetings each week ('Review Lectures'). We will discuss any questions that you either suggest in advance or raise in class on the day. Here is an Introduction to how the course is intended to be followed.

Lecture videos, associated readings and associated Matlab are hosted on the University's LEARN system. You will need an EASE account to access this materials. To access the materials:


Coursework (40%)
There will be two pieces of assessed coursework carrying equal weight. (20% each)

  1. A vision assignment during weeks 3-6, announced by Thursday 28 January and due 4pm Thursday 25 February. There will be an assessed demonstration of the assignment in the Robotics Lab from 9:00-13:00 on Friday 26 February. Marked results will be returned by 10 March.

  2. A robotics assignment during weeks 7-10, announced by Thursday 3 March and due 4pm Thursday 24th March. There will be an assessed demonstration of the assignment in the Robotics Lab from 9:00-13:00 on Friday 25th of March.

The practicals are done in teams of two. For each assignment a demonstration of the results will be required and a single, joint report is to be submitted by one of the students in each team.

Exam (60%)
Here is a sample paper with sample answers.
The lecture and practical contents define the examinable material.

This page is maintained by the course lecturer, Michael Herrmann,, room IF 1.42, ext 517177.

Home : Teaching : Courses 

Informatics Forum, 10 Crichton Street, Edinburgh, EH8 9AB, Scotland, UK
Tel: +44 131 651 5661, Fax: +44 131 651 1426, E-mail:
Please contact our webadmin with any comments or corrections. Logging and Cookies
Unless explicitly stated otherwise, all material is copyright © The University of Edinburgh