Papers on Categorization in Autonomous Agents using Neural Networks

[Christian Scheier]

The following papers deal with the problem of categorization/object
recognition in autonomous agents (mobile robots). 

The papers can be retrieved from:

ftp://claude.ifi.unizh.ch/pub/institute/ailab/techreports/


96_01.ps.gz:

  Categorization in a real-world agent using haptic
	 exploration and active perception 

	Scheier, C. and Lambrinos, D. 

		ABSTRACT

An agent in the real world has to be able to make distinctions
between different types of objects, i.e. it must have the competence
of categorization. In mobile agents categorization is hard to achieve
because there is a large variation in proximal sensory stimulation
originating from the same object. In this paper we extend previous
work on adaptive categorization in autonomous agents. The main idea of
our approach is to include the agent's own actions into the
classification process. In the experiments presented in this paper an
agent equipped with an active vision and an arm-gripper system has to
collect certain types of objects. The agent learns about the objects
by actively exploring them. This exploration results in visual and haptic
information that is used for learning. In essence, the categorization
comes about via evolving reentrant connections between the haptic and
the visual system. Results on the behavioral performance as well as
the underlying internal dynamics are presented. 

95_12.ps.gz:

  Adaptive Classification in Autonomous Agents

	Scheier, C. and Lambrinos, D. 

		ABSTRACT

One of the fundamental tasks facing autonomous robots is to reduce
the many degrees of freedom of the input space by some sorts of
classification mechanism. The sensory stimulation caused by one and
the same object, for instance, varies enormously depending on lighting
conditions, distance from object, orientation and so on. Efforts to
solve this problem, say in classical computer vision, have only had
limited success. In this paper a new approach towards classification 
in autonomous robots is proposed. It's cornerstone is the
integration of the robots own actions into the classification
process. More specifically, correlations through time-linked
independent samples of sensory stimuli and of kinesthetic signals
produced by self-motion of the system form the basis of the category
learning.  Thus, it is suggested that classification should not be
seen as an isolated perceptual (sub-)system but rather as a {\it
sensory-motor coordination} which comes about through a
self-organizing process. These ideas are illustrated with a case study
of an autonomous system that has to learn to distinguish between
different types of objects.  

		
95_05.ps.gz: 
		
 Classification  as Sensory-Motor Coordination: A Case Study 
		on Autonomous Agents.

	Scheier, C. and Pfeifer, R. 

		ABSTRACT

In psychology classification is studied as a separate cognitive
capacity. In the field of autonomous agents the robots are equipped
with perceptual mechanisms for classifying objects in the environment,
either by preprogramming or by some sorts of learning
mechanisms. One of the well-known hard and fundamental problems is the
one of perceptual aliasing, i.e.
that the sensory stimulation caused by one and the same object
varies enormously depending on distance from object, orientation,
lighting conditions, etc. Efforts to solve this problem, say in
classical computer vision, have only had limited success. In this
paper we argue that classification cannot be viewed as a separate
perceptual capacity of an agent but should be seen as a
sensory-motor coordination which comes about through a self-organizing
process. This implies that the whole organism is involved, not only
sensors and neural circuitry. In this perspective, ``action selection''
becomes an integral part of classification. These ideas are
illustrated with a case study of a robot that learns to distinguish
between graspable and non-graspable pegs.



For further informations and papers contact:

-- 

__________________________________________________________________________
 Christian Scheier                       Computer Science Department
 AI Lab                                  University of Zurich
 tel: +41-1-257-4575                     Winterthurerstrasse 190
 fax: +41-1-363-0035                     CH-8057 Switzerland
 http://josef.ifi.unizh.ch/groups/ailab/people/scheier.html
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