Neural mechanisms for learning of attention control and pattern categorization as basis for robot cognition

IEEE International Conference on Intelligent Robots and Systems

Volumn 1, Issue , 2000, Pages 70-75

  Gonçalves, Luiz M, G ^a   Distante, Cosimo ^b   Oliveira, Antonio A F ^c   Wheeler, David ^d   Grupen, Roderic A ^d  

^a LAAS CNRS   (France)

^b UNIVERSITY OF SALENTO   (Italy)

^c FEDERAL UNIVERSITY OF RIO DE JANEIRO   (Brazil)

^d UNIVERSITY OF MASSACHUSETTS   (United States)

Author keywords

[No Author keywords available]

Indexed keywords

BACKPROPAGATION; COGNITIVE SYSTEMS; COMPUTER SIMULATION; COMPUTER VISION; DATA REDUCTION; FEATURE EXTRACTION; IMAGE PROCESSING; MANIPULATORS; MULTILAYER NEURAL NETWORKS; NEURAL NETWORKS; ROBOTICS; SENSOR DATA FUSION;

ASSOCIATIVE MEMORY; ATTENTION CONTROL; ROBOT COGNITION; SALIENCE MAPS;

ROBOT LEARNING;

EID: 0034447379     PISSN: None     EISSN: None     Source Type: Journal    
DOI: 10.1109/IROS.2000.894584     Document Type: Article

References (11)

1. A reinforcement learning approach for attentional control based on a multi-modal sensory feedback
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3. Attentive mechanisms for dynamic and static scene analysis
4. An active vision architecture based on iconic representations
5. A direct adaptive method for faster backpropagation learning: The rprop algorithm
6. A model of attention-guided visual perception and recognition
7. Task-dependent learning of attention
8. Complex feature recognition: A Bayesian approach for learning to recognize objects