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Autonomous Robotics: Action, Perception, and Cognition

The exam results and final grades are now available under 'course documents'. Please let us know if you find any mistakes in the calculated bonus marks.

Neural computation is concerned with the discovery of new solutions to technical problems of information processing. These solutions are sought based on analogies with nervous systems and the behaviour of organisms.

This course focuses on three exemplary problems to illustrate this approach:
(a) Artificial action (autonomous robotics);
(b) Artificial perception (robot vision);
(c) Artificial cognition (simplest cognitive capabilities of autonomous robots such as decision making, scene representation, working memory, sequence generation, behavioral organization).

The main method is nonlinear dynamical systems applied to neural networks, leading to Dynamic Field Theory and neural dynamics.



Course type
6 CP
Summer Term 2015


Takes place every week on Thursday from 14:15 to 16:00 in room NB 3/57.
First appointment is on 23.04.2015
Takes place every week on Thursday from 16:15 to 17:00 in room NB 3/57.
First appointment is on 07.05.2015
Takes place on 14.08.2015 from 14:00 to 16:00 in room HZO 100.


Usually, we upload an exercise sheet after each lecture. This sheet has to be handed in before the lecture of the following week (alternatively, you can hand it in via email to Oliver Lomp), which gives you a week to work on the solutions.

After collecting your solutions, we take a week to correct them and then discuss them in the exercise session following the lecture.

Exercises are corrected, and exercise sessions lead by Oliver Lomp.

Further reading

In case you are interested in additional material that goes beyond the scope of the course, have a look at our robotics school page. It contains more exercises, reading materials, slides and talks that have some overlap with the lecture.


Lecture slides Lecture Notes Dynamical Systems Tutorial
Lecture slides Lecture notes: DFT core lecture part 2
Lecture slides Lecture notes: Scene Representation
Lecture slides Lecture notes: Object recognition
Lecture slides Exam results and final grades
Exercises Exercise 3
Lecture slides Essay reading (Fajen et al., 2003)
Lecture slides Lecture notes Movement generation for robot arms
Lecture slides Lecture notes Toward perception and cognition... Dynamic Field Theory
Exercises Reading for Exercise 4 (Reimann, Lins, Schöner, 2015)
Exercises Exercise 4 (due June 25, 2015)
Lecture slides Lecture notes: How to organize behaviors in DFT
Exercises Exercise 6 (due 9 July 2015)
Lecture slides Lecture notes: DFT core lecture
Exercises Exercise 5 (due 2 July 2015)
Lecture slides Lecture Notes Second Order Attractor Dynamics Approach
Lecture slides Essay Assignment (due June 11)
Lecture slides Rules for exercises, essay, exam
Lecture slides Lecture Notes Introduction
Exercises Reading for Exercise 1
Exercises Exercise 1
Lecture slides Organizational notes
Lecture slides Lecture Notes Attractor Dynamics Approach
Exercises Exercise 2

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