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Autonomous Robotics: Action, Perception, and Cognition

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 2019


Takes place every week on Thursday from 14:15 to 16:00 in room NB 3/57.
First appointment is on 11.04.2019
Last appointment is on 11.07.2019
Takes place every week on Thursday from 16:15 to 17:00 in room NB 3/57.
First appointment is on 11.04.2019
Last appointment is on 11.07.2019


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 Mathis Richter), 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 Mathis Richter.

Further reading

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


Document Rules for obtaining credit
Lecture slides Organizational information
Lecture slides Introduction to the topic
Lecture slides Dynamical systems tutorial
Lecture slides Attractor dynamics approach to vehicle motion planning
Lecture slides Attractor dynamics for vehicle path planning: sub-symbolic approach
Exercises Exercise 1: obstacle avoidance
Document Reading for exercise 1: Bicho, Mallet, Schöner
Document General background reading for the attractor dynamics approach (only the first half of the article is relevant)
Lecture slides Attractor dynamics to account for pedestrian paths
Lecture slides Attractor dynamics for vehicle motion: second order approach
Lecture slides Other approaches to motion planning
Exercises Exercise 2 (longer exercise)
Document Paper for exercise 2 by Fajen et al.
Document Paper for exercise 2: Arkin
Lecture slides Movement generation for robot arms
Lecture slides The degree of freedom problem
Exercises Exercise 3: Uncontrolled Manifold
Lecture slides Timing and coordination
Exercises Exercise 4 on timing
Document Reading for exercise 4
Document Supplementary reading for exercise 4 (Amari 1977)
Lecture slides Dynamic Movement Primitives
Document Optional reading for DMP
Lecture slides Robotic motor control
Lecture slides Human motor control
Lecture slides Summary of main conceptual points of the course

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