two papers on evolutionary robotics

[stefano@kant.irmkant.rm.cnr.it]

Papers available via WWW / FTP: 

Keywords: Evolutionary Robotics, Neural Networks, Genetic Algorithms,
          Autonomous Robots, Noise. 
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              EVOLVING NON-TRIVIAL BEHAVIORS ON REAL ROBOTS:
                  AN AUTONOMOUS ROBOT THAT PICK UP OBJECTS

                      Stefano Nolfi   Domenico Parisi
      ***Institute of Psychology, National Research Council, Rome, Italy
                e-mail: stefano at kant.irmkant.rm.cnr.it
                        domenico at kant.irmkant.rm.cnr.it
                                 
                                Abstract

Recently, a new approach that involves a  form of simulated evolution has been
proposed for the building of autonomous robots. However, it is still not clear
if this approach may be adequate to  face real life problems. In this paper we
show how control systems that perform a  non-trivial sequence of behaviors can
be obtained with this  methodology by  carefully designing  the  conditions in
which the  evolutionary process operates.  In  the experiment described in the
paper,  a  mobile robot  is trained  to  locate, recognize, and grasp a target
object. The controller  of  the robot  has been evolved in simulation and then
downloaded and tested on the real robot. 

to appear in:
G. Soda (Ed.) Proceedings of the Fourth Congress of the Italian 
Association for Artificial Intelligence, Firenze, 11-13 October, 
Springer Verlag.

http://kant.irmkant.rm.cnr.it/public.html    
or
ftp-server: kant.irmkant.rm.cnr.it (150.146.7.5)
ftp-file  : nolfi.gripper.ps.Z
(the file is 0.36 MB)

for the homepage of our research group:
http://kant.irmkant.rm.cnr.it/gral.html


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     EVOLVING MOBILE ROBOTS IN SIMULATED AND REAL ENVIRONMENTS

       Orazio Miglino*, Henrik Hautop Lund**, Stefano Nolfi***

       *Department of Psychology, University of Palermo, Italy
       		e-mail: orazio at caio.irmkant.rm.cnr.it
      **Department of Computer Science, Aarhus University, Denmark
                e-mail: henrik at caio.irmkant.rm.cnr.it
      ***Institute of Psychology, National Research Council, Rome, Italy
                e-mail: stefano at kant.irmkant.rm.cnr.it

                                Abstract

The problem of the validity of simulation is particularly relevant for 
methodologies  that use machine learning techniques to develop control 
systems for autonomous robots, like, for instance, the Artificial Life 
approach named Evolutionary Robotics. 
In fact, despite that it has been demonstrated that training or 
evolving robots in the real environment is possible, the number of 
trials needed to test the system discourage the use of physical robots 
during the training period.  
By evolving neural controllers for a Khepera robot in computer 
simulations and then transferring the obtained agents in the real 
environment we will show that: 
(a) an accurate model of a particular robot-environment dynamics 
can be built by sampling the real world through the sensors and the 
actuators of the robot; 
(b) the performance gap between the obtained behaviors in simulated and 
real environment may be significantly reduced by introducing a 
"conservative" form of noise; 
(c) if a decrease in performance is observed when the system is 
transferred to the real environment, successful and robust results can 
be obtained by continuing the  evolutionary process in the real 
environment for few generations.

Technical Report, Institute of Psychology, C.N.R, Rome.

http://kant.irmkant.rm.cnr.it/public.html 
or
ftp-server: kant.irmkant.rm.cnr.it (150.146.7.5)
ftp-file  : miglino.sim-real.ps.Z
(the file is 2.67 MB)

for the homepage of our research group:
http://kant.irmkant.rm.cnr.it/gral.html


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Stefano Nolfi
Institute of Psychology
National Research Council
e-mail: stefano at kant.irmkant.rm.cnr.it