Evolving dynamical neural networks for adaptive behavior
Adaptive Behavior
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
On the dynamics of active categorisation of different objects shape through tactile sensors
ECAL'09 Proceedings of the 10th European conference on Advances in artificial life: Darwin meets von Neumann - Volume Part I
| Hi-index | 0.00 |
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 cognitive 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 discriminate between spherical and ellipsoid objects. The results of this work demonstrate that evolved continuous time nonlinear neural controllers can bring forth strategies to allow the arm to effectively solve the discrimination task.