The Acquisition of New Categories through Grounded Symbols: An Extended Connectionist Model

Greco, Alberto; Riga, Thomas; Cangelosi, Angelo

doi:10.1007/3-540-44989-2_91

Cited by 6 publications

(3 citation statements)

References 7 publications

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“…The modular organization of the hidden-output connections has been designed to allow the robotic agents to learn combinations of the action words. Such a modular, connectionist architecture has been demonstrated to be necessary for action/language tasks requiring the acquisition of higher order categories via combinations of their names (Greco, Riga, & Cangelosi, 2003).…”

Section: Neural Network Controllermentioning

confidence: 99%

An Embodied Model for Sensorimotor Grounding and Grounding Transfer: Experiments With Epigenetic Robots

2006

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The grounding of symbols in computational models of linguistic abilities is one of the fundamental properties of psychologically plausible cognitive models. In this article, we present an embodied model for the grounding of language in action based on epigenetic robots. Epigenetic robotics is one of the new cognitive modeling approaches to modeling autonomous mental development. The robot model is based on an integrative vision of language in which linguistic abilities are strictly dependent on and grounded in other behaviors and skills. It uses simulated robots that learn through imitation the names of basic actions. Robots also learn higher order action concepts through the process of grounding transfer. The simulation demonstrates how new, higher order behavioral abilities can be autonomously built on previously grounded basic action categories following linguistic interaction with human users.

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Section: Neural Network Controllermentioning

confidence: 99%

An Embodied Model for Sensorimotor Grounding and Grounding Transfer: Experiments With Epigenetic Robots

2006

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“…Six object category output category output units encode the category names. This modular organization of the hidden unit connectivity is needed to allow the compositional learning of higher-order concepts, as in [14].…”

Section: B Neural Network Controllermentioning

confidence: 99%

Learning of composite actions and visual categories via grounded linguistic instructions: Humanoid robot simulations

Chuang

Lin

Cangelosi

2012

The 2012 International Joint Conference on Neural Networks (IJCNN)

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This paper presents a cognitive learning system for robot recognition and composite action learning. The cognitive system of the robot is an artificial neural network trained to recognize and handle objects through imitation and backpropagation algorithm learning. The robot is first trained to learn the representation of action words, object categories and grounded language understanding. Following a human tutor's linguistic instructions, the robot autonomously transfers the grounding form directly basics knowledge to new higher level composite knowledge.

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“…In [18] authors investigated the transfer of grounding from names of geometric shapes (e.g., circles, ellipses, squares and rectangles) to the superposed categories ("symmetric" and "asymmetric"). This model was then expanded in [19] to deal with larger category sets, and to look at different aspects of the transfer of grounding. In [20] connectionist simulations of the grounding transfer mechanism were performed.…”

Section: The Symbol Grounding Problemmentioning

confidence: 99%

Towards the grounding of abstract words: A Neural Network model for cognitive robots

Stramandinoli

Cangelosi

Marocco

2011

The 2011 International Joint Conference on Neural Networks

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In this paper, a model based on Artificial Neural Networks (ANNs) extends the symbol grounding mechanism to abstract words for cognitive robots. The aim of this work is to obtain a semantic representation of abstract concepts through the grounding in sensorimotor experiences for a humanoid robotic platform. Simulation experiments have been developed on a software environment for the iCub robot. Words that express general actions with a sensorimotor component are first taught to the simulated robot. During the training stage the robot first learns to perform a set of basic action primitives through the mechanism of direct grounding. Subsequently, the grounding of action primitives, acquired via direct sensorimotor experience, is transferred to higher-order words via linguistic descriptions. The idea is that by combining words grounded in sensorimotor experience the simulated robot can acquire more abstract concepts. The experiments aim to teach the robot the meaning of abstract words by making it experience sensorimotor actions. The iCub humanoid robot will be used for testing experiments on a real robotic architecture.

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The Acquisition of New Categories through Grounded Symbols: An Extended Connectionist Model

Cited by 6 publications

References 7 publications

An Embodied Model for Sensorimotor Grounding and Grounding Transfer: Experiments With Epigenetic Robots

An Embodied Model for Sensorimotor Grounding and Grounding Transfer: Experiments With Epigenetic Robots

Learning of composite actions and visual categories via grounded linguistic instructions: Humanoid robot simulations

Towards the grounding of abstract words: A Neural Network model for cognitive robots

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