Robotic object recognition using vision and touch
Robotic object recognition using vision and touch
Connectionist learning for control: an overview
Neural networks for control
NIPS-3 Proceedings of the 1990 conference on Advances in neural information processing systems 3
A competitive modular connectionist architecture
NIPS-3 Proceedings of the 1990 conference on Advances in neural information processing systems 3
Evaluation of adaptive mixtures of competing experts
NIPS-3 Proceedings of the 1990 conference on Advances in neural information processing systems 3
Multiple model-based reinforcement learning
Neural Computation
MOSAIC Model for Sensorimotor Learning and Control
Neural Computation
Modular learning schemes for visual robot control
Biomimetic Neural Learning for Intelligent Robots
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For recognition and control of multiple manipulated objects, we present two learning schemes for neuralnetwork controllers based on feedback-error-learning and modular architecture. In both schemes, the network consists of a recognition network and modular control networks. In the first scheme, a Gating Network is trained to acquire object-specific representations for recognition of a number of objects (or sets of objects). In the second scheme, an Estimation Network is trained to acquire function-specific, rather than object-specific, representations which directly estimate physical parameters. Both recognition networks are trained to identify manipulated objects using somatic and/or visual information. After learning, appropriate motor commands for manipulation of each object are issued by the control networks which have a modular structure. By simulation of simple examples, the potential advantages and disadvantages of the two schemes are examined.