Using Kinematically Complex Robots for Case Studies in Embodied Cognition
Yohannes Kassahun, Mark Edgington, José de Gea Fernández, Elsa Andrea Kirchner, Dirk Spenneberg, Frank Kirchner
In Proceedings of the 9th International Conference on Climbing and Walking Robots (CLAWAR 2006), (CLAWAR), Brussels, o.A., pages 258, 2006.
Abstract
:
We present two case studies in embodied
cognition which use kinematically complex robots for
spatial cognition and concept forming. The first case
study involves substrate classification on the basis of pri-
marily proprioceptive data. During walking over vari-
ous substrates a legged robot generates certain substrate
specific sensory motor patterns. The acquired data is
used for training a growing self-organizing neural net-
work, which is connected with a standard output layer
representing different substrates. The second case study
is concerned with a recognition system which learns to
recognize objects based on multimodal sensorimotor co-
ordination. The sensorimotor coordination is generated
through interaction with the environment. The system
uses a learning architecture which is composed of reac-
tive and deliberative layers. The reactive layer consists
of a database of behaviors that are modulated to produce
a desired behavior. We have implemented in the learning
architecture an object manipulation behavior inspired by
the concept that infants learn about their environment
through manipulation [1]. While manipulating objects,
the agent records both proprioceptive data and extero-
ceptive data. Both of these types of data are combined
and statistically analyzed in order to extract important
parameters that distinctively describe the object being
manipulated. This data is then clustered using the stan-
dard k-means algorithm and the resulting clusters are
labeled. The labeling is used to train a radial basis func-
tion network for classifying the clusters. It has been
found that the trained neural network is able to clas-
sify objects even when only partial sensory data is avail-
able to the system. Our preliminary results in both case
studies demonstrate that kinematically complex robots
are suitable for learning about their environment from
experience and provide a new useful class of propriocep-
tive information in contrast to wheeled systems.
Files:
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