Evolving dynamical neural networks for adaptive behavior
Adaptive Behavior
Dynamic representation of decision-making
Mind as motion
Embedded neural networks: exploiting constraints
Neural Networks - Special issue on neural control and robotics: biology and technology
Understanding intelligence
Genetic Algorithms in Search, Optimization and Machine Learning
Genetic Algorithms in Search, Optimization and Machine Learning
Active perception: a sensorimotor account of object categorization
ICSAB Proceedings of the seventh international conference on simulation of adaptive behavior on From animals to animats
Evolutionary Robotics: The Biology, Intelligence, and Technology of Self-Organizing Machines
Evolutionary Robotics: The Biology, Intelligence, and Technology of Self-Organizing Machines
Neural network models of haptic shape perception
Robotics and Autonomous Systems
Adaptive Behavior - Animals, Animats, Software Agents, Robots, Adaptive Systems
The iCub cognitive humanoid robot: an open-system research platform for enactive cognition
50 years of artificial intelligence
The facilitatory role of linguistic instructions on developing manipulation skills
IEEE Computational Intelligence Magazine
Communications of the ACM
Evolutionary robotics approach to odor source localization
Neurocomputing
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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 neurocontrolled 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.