Active Categorical Perception of Object Shapes in a Simulated Anthropomorphic Robotic Arm

Active perception refers to a theoretical approach to the study of perception grounded on the idea that perceiving is a way of acting, rather than a process whereby the brain constructs an internal representation of the world. The operational principles of active perception can be effectively tested...

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Bibliographic Details
Published in:IEEE transactions on evolutionary computation 2010-12, Vol.14 (6), p.885-899
Main Authors: Tuci, Elio, Massera, Gianluca, Nolfi, Stefano
Format: Article
Language:English
Subjects:
Anthropomorphism
Applied sciences
Artificial intelligence
Artificial neural networks
Biological neural networks
Brain
Brain modeling
Buildings
categorical perception
Cognitive robotics
Computer science
control theory
systems
Connectionism. Neural networks
Control theory. Systems
Ellipsoids
evolutionary robotics
Exact sciences and technology
Mathematical models
Optimization
Perception
Robot sensing systems
Robotics
Robots
Shape
Tactile
Tactile sensors
Tasks
Testing
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Summary:Active perception refers to a theoretical approach to the study of perception grounded on the idea that perceiving is a way of acting, rather than a process whereby the brain constructs an internal representation of the world. The operational principles of active perception can be effectively tested by building robot-based models in which the relationship between perceptual categories and the body-environment interactions can be experimentally manipulated. In this paper, we study the mechanisms of tactile perception in a task in which a neuro-controlled anthropomorphic robotic arm, equipped with coarse-grained tactile sensors, is required to perceptually categorize spherical and ellipsoid objects. We show that best individuals, synthesized by artificial evolution techniques, develop a close to optimal ability to discriminate the shape of the objects as well as an ability to generalize their skill in new circumstances. The results show that the agents solve the categorization task in an effective and robust way by self-selecting the required information through action and by integrating experienced sensory-motor states over time.
ISSN:1089-778X
1941-0026
DOI:10.1109/TEVC.2010.2046174