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Autonomous Robotics: Action, Perception, and Cognition

The grades for the semester are up! (level NB3 - or write js an email). 'Scheine' are now available in our administrative office (level NB3 - Sekretariat); Klausureinsicht: Friday, 25th at 14:00 in NB02/73 or via appointment - write js an email).

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.

Lecturers

Details

Course type
Lectures
Credits
6 CP
Term
Summer Term 2017

Dates

Lecture
Takes place every week on Thursday from 14.15 to 16.00 in room NB 3/57.
First appointment is on 20.04.2017
Last appointment is on 27.07.2017
Exercise
Takes place every week on Thursday from 16.15 to 17.00 in room NB 3/57.
First appointment is on 27.04.2017
Last appointment is on 27.07.2017

Exercises

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 Jean-Stephane Jokeit), 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 Jean-Stephane Jokeit.

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.

Documents

Lecture slides Organizational notes
Lecture slides Introductory lecture
Document Rules for credit and marks
Lecture slides Introductory lecture part 2: Analogies to human movement
Lecture slides Attractor dynamic approach: vehicle motion
Exercises Exercise 1: reading on attractor dynamics approach

To access the referenced paper: SchonerDose92bw.pdf

Lecture slides Attractor dynamics for vehicle motion: subsymbolic approach
Exercises Exercise 2 obstacle avoidance
Lecture slides Dynamical Systems Tutorial
Exercises Exercise 3: Dynamical Systems
Lecture slides Dynamical systems tutorial part 2
Lecture slides Attractor Dynamics approach: 2nd order dynamics on low-level vehicle
Exercises Exercise 4: Reading on applying attractor dynamics approach to human path generation
Document Paper needed for Exercise 4
Lecture slides How to organize behaviors in DFT
Exercises Essay exercise: Reading
Document Reading for the essay exercise
Lecture slides Dynamic field theory (DFT)
Exercises Exercise 5 DFT
Lecture slides Lecture on manipulator kinematics and attractor dynamics
Exercises Exercise 6
Lecture slides Timing and coordination
Exercises Exercise 7: timing
Document Matlab code for Exercise 7
Document Auxiliary Matlab code for exercise 7
Document Paper for exercise 7
Lecture slides Lecture on the Reaching Architecture.

 Alternative: interactive flash link (RECOMMENDED)
Lecture slides Lecture on Motor Control

About the INI

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.

Contact

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

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

sekretariat@ini.rub.de
www.ini.rub.de