E-learning course overview

This course will take place in remote teaching form. All interaction takes place through this webpage (rather than through Moodle). 

The course is open to all students. Please register by  follow the link to "e-learing", creating an account (if you don't have one yet). When you create an account, please enter your degree program specifically (e.g. Master in Angewandte Informatik). Just listing "Master" doesn't help us. We really like to know what disciplinar you come from so we can make sure we have the right offering given your background.

Registering in this way enables you to see all material, access the links to the ZOOM lectures, and upload your solutions to exercises. Some of the material is visible also to unregistered visitors. 

At the scheduled lecture times, we will have live life video-streamed lectures. Registered participants will be able to see the ZOOM link under "live sessions" in the e-learning portion of this web page. This works best if you use the ZOOM app, but a web based ZOOM interface works too. You can use audio to ask questions and are welcome to activate your video camera as well, especially during discussion. 

Live sessions will start on Thursday, 14th of April 2022 at 14:15. 

The lectures will be recorded and will be available for asynchronous viewing. Participating in the live session and asking questions is highly encouraged, however. The discussion portion of the lecture is not put online. 

We will have live sessions for the exercises at the listed times. The exercise sessions will not be recorded.  

All course material will be on this webpage, including links to all the video material, to the lecture slides, exercises, and readings.

After registering, you will be able to upload your solutions to the exercises and will also be able to see your results once your submission has been corrected. You will be also be able to use discussion forums to ask questions synchronously and interact with your peers. 

Autonomous robotics is an interdisciplinary research field in which embodied systems equipped with their own sensors and with actuators generate behavior that is not completely pre-programmed. Autonomous robotics thus entails perception, movement generation, as well as core elements of cognition such as making decisions, planning, and integrating multiple constraints.

This course touches on various approaches to this interdisciplinary problem. In the first half of the course, the main emphais will be on dynamical systems methods for generating movement in vehicles.  The main focus of the course is, however, on solutions to autonomous movement generation that are inspired by analogies with how nervous systems generate movement. The second half of the course will review core problems in human movement  science, including the degree of freedom problem, coordination, motor control, and the relex control of muscles. 



Course type
6 CP
Summer Term 2022
e-learning course available


Takes place every week on Thursday from 14:15 to 16:00.
First appointment is on 14.04.2022
Last appointment is on 14.07.2022
Takes place every week on Thursday from 16:15 to 17:00.
First appointment is on 21.04.2022
Last appointment is on 14.07.2022


The emphasis of the course is on learning concepts, practicing interdisciplinary scholarship including reading and writing at a scientific and technical level. Mathematical concepts are used throughout, so understanding these concepts is important. Mathematical skills are not critical to mastering the material, but helpful. The mathematics is mostly from the qualitative theory of dynamical systems, attractors and their instabilities. Short tutorials on some of these concepts will be provided. 


The course is accompanied by exercises, which will be posted weekly. Participants will upload their solutions, which will be corrected and marked (for bonus points). The exercises and their solutions will be discussed by Rachid Ramadan in the weekly exercise live session.

Further reading

Readings will be posted on this web page. Also have a look at the web page of the Dynamic  Field Theory community that is interested in related problems and solutions. There you find more exercises, reading material, slides and lecture videos that have some overlap with the lecture.

Teaching Units

Lecture slides Introduction
Video Introduction

This video is last year's rendition of the introductory lecture. Unfortunately, Zoom crashed during this year's delivery of this lecture, so a new video could not be generated. The contents is quite similar, however. The video and audio quality is not great, will be better on the new videos of the current course. 

Dynamical systems tutorial
Lecture slides Dynamical systems tutorial

This lecture gives a very fast conceptual introduction into key ideas of dynamical systems theory, starting from zero, but going to the Hopf theorem within a single lecture. The conceptual ideas are used throughout the course to design dynamical systems that generated robotic behavior.

Video Dynamical systems tutorial
Document simple simulator forward Euler

This Matlab code illustrates the core idea of the numerical solution of differential equations in a foward Euler procedure. 

Document ODE simulator

This variant of the simple simulator for differential equations reflects how such simulators would more typically be implemented in Matlab. 

Exercises Exercise 1 on the dynamical systems tutorial
Attractor dynamics for vehicle motion planning
Lecture slides Attractor dynamics for vehicle motion planning

This lecture makes use of the concepts of attractors, repellors, and their bifurcations to enable a robotic agent to generate motion toward targets, avoiding obstacles. I also review empirical work from Bill Warren's lab that shows that human movement paths can be well described in these terms.

Video Attractor dynamics for vehicle motion planning
Exercises Exercise 2 on the attractor dynamics approach to vehicle motion planning
Document Background reading

Supports the lecture on the attractor dynamics approach for vehicle motion planning and the associated Exercise 2. [Read only until page 223 before "(3) Neural field dynamics"]

Lecture slides Attractor dynamics for vehicle motion planning: sub-symbolic approach

I review two methods for how to use attractor dynamics to generate paths for autonomous vehicles based on low-level sensory information.

Video Attractor dynamics for vehicle motion planning: sub-symbolic approach
Exercises Exercise 3: attractor dynamics for vehicle motioin: sub-symbolic approach
Document Reading for Exercise 3

Also a back-ground reading for the associated lecture on the sub-symbolic variant of the attractor dynamics approach. 

Exercises Exercise 4: Invariance of the sub-symbolic attractor dynamics approach to obstacle avoidance
Exercises Essay exercise 5: Attractor dynamics approach to robot cooperation
Document Reading for Essay Exercise 5: Machado et al. 2019
Robot vehicle motion planning: relation to other approaches
Lecture slides Other approaches to vehicle motion planning

In this lecture I define some general concepts to characterize different approaches to motion planning and review a small number of approaches other than the attractor dynamics method of the previous lecture to position the attractor dynamics approach in that landscape. These include the potential field approach, the Borenstein-Koren vector-field method, and the dynamic window approach.

Video Other approaches to vehicle motion planning
Lecture slides Navigation

This lecture introduces into the problem of navigation, knowing where you are and how to get places, and links this to the problem of movement planning. I first clarify the concepts involved and then review a dynamical systems based approach to perform navigation, both the building of a map and its use to go to targets. I then provide some neuroscience background for this, reviewing the basic facts of place, grid, and heading direction cells and point to concepts from neuroscience are used to solve technical navigation problems.

Video Navigation
Physiology of human movement
Lecture slides Human motor control

In this lecture, Dr. Lei Zhang reviews properties of human voluntary movement of the upper limb and gives a survey over the neurophysiological foundations of human motor control.

Video Human motor control
Timing, coordination, and movement primitives
Lecture slides Timing and coordination

This lecture discusses the problem of creating time courses for robotic motion, first in conventional robotic planning, then in autonomous robots. It reviews timing and coordination of human movement, which is a source of inspiration and concepts for conceiving of coordination as emerging from coupled limit cycle oscillators. A few robotic implementations of such dynamic ideas are reviewed, culminating the the landmark work of Aude Billard (which is not explained in any detail, however).

Document Background reading for timing and coordination
Video Timing and coordination
Lecture slides Dynamic movement primitives

In this short lecture, I review the core idea behind the notion of Dynamic Movement primitives that goes back to Auke Ijspeert's work with Stefan Schaal and others. I only touch the basics here. This lecture builds on the lecture on timing and coordination that provides context.

Video Dynamic Movement Primitives
Document Background paper for Dynamic Movement Primitives
Motor control
Lecture slides Motor control

This is a very brief and not too deep survey over issues in controlling robot arms including the dynamics of kinematics chains, principles of control, with repeated reference and contrast to human motor control. Most topics are only roughly outlined as a guide to this rather large field.

Video Motor control
Lecture slides Summary

to help with exam preparation

The Institut für Neuroinformatik (INI) is a central research unit of the Ruhr-Universität Bochum. We aim to understand the fundamental principles through which organisms generate behavior and cognition while linked to their environments through sensory systems and while acting in those environments through effector systems. Inspired by our insights into such natural cognitive systems, we seek new solutions to problems of information processing in artificial cognitive systems. We draw from a variety of disciplines that include experimental approaches from psychology and neurophysiology as well as theoretical approaches from physics, mathematics, electrical engineering and applied computer science, in particular machine learning, artificial intelligence, and computer vision.

Universitätsstr. 150, Building NB, Room 3/32
D-44801 Bochum, Germany

Tel: (+49) 234 32-28967
Fax: (+49) 234 32-14210