USING EMERGENT MODULARITY TO DEVELOP CONTROL SYSTEMS FOR MOBILE ROBOTS 

                             Stefano Nolfi


A new way of building control systems, known as behavior based robotics, has 
recently been proposed  to  overcome  the difficulties of the traditional AI 
approach to robotics. This new approach  is based upon the idea of providing 
the  robot  with  a  range  of  simple behaviors and letting the environment 
determine  which  behavior  should  have control  at any given time. We will 
present  a  set  of  experiments  in  which  neural  networks with different 
architectures  have  been trained to control a mobile robot designed to keep 
an  arena  clear  by picking-up trash objects and releasing them outside the 
arena. Controller weights  are  selected  using  a form of genetic algorithm 
and  do  not  change  during the lifetime (i.e. no learning occurs). We will 
compare,  in  simulation  and  on  a  real  robot,  five  different  network 
architectures  and  will  show  that a network which allows for fine-grained 
modularity  achieves  significantly  better  performance.  By  comparing the 
functionality  of each network module and its interaction with a description 
of  the  simple behavior components, we will show that it is not possible to 
find  simple  correlations;  rather,  module  switching  and  interaction is 
correlated  with  low-level  sensory-motor  mappings. This  implies that the 
engineering-oriented approach  to behavior-based robotics might have serious 
limitations  because  it  is  difficult  to  know in advance the appropriate 
mappings between behavior components and  sensory-motor activity for complex 
tasks.