CFP: Scalable Robotic Applications of Neural Networks

[Patrick van der Smagt]

	Scalable Robotic Applications of Neural Networks
	================================================
	Special Issue of "Applied Intelligence"
	Editors: Patrick van der Smagt and Daniel Bullock
	http://www.robotic.dlr.de/Smagt/CFP/

In this special issue, we want to address the question of whether there
are real robotic problems that can be better solved using existing
neuro-computational principles than by using other standard engineering
techniques.  Where sensory-motor systems cannot be explicitly modeled,
the brain's success leads us to expect that approaches based on
adaptive neural control will someday provide a technically sound
alternative.  However, today's robotic engineers are appropriately
skeptical regarding use of neural network principles because of the
apparent scarcity of published research that demonstrates scalability
of solutions to complex robotic control tasks.  For example, like many
traditional approaches, many neural network control strategies do not
scale well from a two-degree-of-freedom robot arm to a seven
degree-of-freedom system.


For this special issue, papers are sought that: 

1. introduce or review biologically plausible models of sensory-motor
control that truly integrate action and perception;

2. newly apply such models to the control of realistic robots,
especially manipulators;

3. describe hybrid robot control methodologies that incorporate
cerebellar or other neuro-computational models (e.g., vision);

4. provide a case history that clearly defines, or illustrates
overcoming of, barriers to successful competition by neural network
models for robot control.


The scalability of applications of biological models can only be fully
assessed in the context of demonstrations that are representative of
real-world complexities.  Therefore, results on real hardware will
generally be preferred.  However, results on well-simulated complex
problems will be preferred to results with hardware (e.g., 2 DoF robot
arms) that can already be optimally controlled with conventional,
non-neural techniques.


Special Issue Editors:
----------------------
Patrick van der Smagt
Institute of Robotics and System Dynamics
DLR (German Aerospace Research Center) Oberpfaffenhofen, Germany
smagt at dlr.de
http://www.robotic.dlr.de/Smagt/


Daniel Bullock
Cognitive and Neural Systems Department
Boston University
danb at cns.bu.edu
http://cns-web.bu.edu/faculty.html#bullock


Editor in Chief:
----------------
Moonis Ali
Department of Computer Science
Southwest Texas State University
ma04 at swt.edu
http://www.cs.swt.edu



Submission
==========
Deadline for submitting papers: Jan 15, 2000. The journal's
instructions to authors can be read at
http://www.robotic.dlr.de/Smagt/CFP/ifa.html.  For this special issue,
*all* papers must be submitted by the above date in Postscript or PDF
format via email to smagt at dlr.de.  Hardcopy submissions will not be
accepted.

To the extent that time permits, the editors will invite short external
commentaries (to be published in the same issue) on the accepted papers. 
Submitters should inform the editors if they do *not* want their paper
treated in an invited commentary. Suggestions of expert reviewers and
commentators are welcome.


About the journal
=================
The objective of Applied Intelligence (IJAI) is to provide a medium
for exchanging applied research on intelligent systems and
technological achievements.  IJAI is currently in its eighth year of
publication.  In order to meet the demand, the publication frequency
has been increased from four to six issues per year effective 1998.
IJAI is abstracted and/or indexed by twenty indexing publications.
See http://www.wkap.nl/journals/apin
-- 
Dr Patrick van der Smagt                phone +49 8153 281152, fax -34
DLR/Institute of Robotics and System Dynamics             smagt at dlr.de
P.O.Box 1116, 82230 Wessling, Germany     http://www.robotic.de/Smagt/