Autonomous Robotics: Action, Perception and Cognition

NUMMER:	310501
KÜRZEL:	AutRob
MODULBEAUFTRAGTE:R:	Prof. Dr. Gregor Schöner
DOZENT:IN:	Prof. Dr. Gregor Schöner
FAKULTÄT:	Institut für Neuroinformatik (RUB)
SPRACHE:	Englisch
SWS:	3 SWS
CREDITS:	6 CP
ANGEBOTEN IM:	jedes Sommersemester

PRÜFUNGEN

FORM:	mündlich
TERMIN:	Siehe Prüfungsamt.

LERNFORM

Vorlesung mit Übung

LERNZIELE

After the successful completion of this course the students:
∙ are familiar with the concepts in dynamical systems theory and can use them practically,
∙ know the mathematical models of movement generation
∙ have practice in reading and writing academic research papers.

INHALT

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 preprogrammed.
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. In
fact, 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.

VORAUSSETZUNGEN CREDITS

Bestandene mündliche Prüfung

LITERATUR

1. Valentino Braitenberg: Vehicles. Experiments in Synthetic Psychology, MIT Press, Cambridge,
Mass 1984
2. Gregor Schöner, Michael Dose, Christoph Engels: Dynamics of behavior: Theory and
applications for autonomous robotic architectures. Robotics and Autonomous Systems,
16:213-245 (1995)
3. Stephan K. U. Zibner, Christian Faubel, Ioannis Iossifidis, and Gregor Schöner: Dynamic
Neural Fields as Building Blocks of a Cortex-Inspired Architecture for Robotic Scene
Representation. IEEE Transactions Autonomous Mental Development 3:74-91 (2011)