| 1. |
- Krunic, Verica, et al.
(författare)
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Affordance based word-to-meaning association
- 2009
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Ingår i: ICRA. - : VDE Verlag GmbH. - 9781424427888 ; , s. 4138-4143
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Konferensbidrag (refereegranskat)abstract
- This paper presents a method to associate meanings to words in manipulation tasks. We base our model on an affordance network, i.e., a mapping between robot actions, robot perceptions and the perceived effects of these actions upon objects. We extend the affordance model to incorporate words. Using verbal descriptions of a task, the model uses temporal co-occurrence to create links between speech utterances and the involved objects, actions and effects. We show that the robot is able form useful word-to-meaning associations, even without considering grammatical structure in the learning process and in the presence of recognition errors. These word-to-meaning associations are embedded in the robot's own understanding of its actions. Thus they can be directly used to instruct the robot to perform tasks and also allow to incorporate context in the speech recognition task.
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| 2. |
- Krunic, Verica, et al.
(författare)
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Associating word descriptions to learned manipulation task models
- 2008
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Ingår i: IEEE/RSJ International Conference on Intelligent RObots and Systems (IROS). - Nice, France.
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Konferensbidrag (refereegranskat)abstract
- This paper presents a method to associate meanings to words in manipulation tasks. We base our model on an affordance network, i.e., a mapping between robot actions, robot perceptions and the perceived effects of these actions upon objects. This knowledge is acquired by the robot in an unsupervised way by self-interaction with the environment. When a human user is involved in the process and describes a particular task, the robot can form associations between the (co-occurrence of) speech utterances and the involved objects, actions and effects. We extend the affordance model to incorporate a simple description of speech as a set of words. We show that, across many experiences, the robot is able form useful word-to-meaning associations, even without considering grammatical structure in the learning process and in the presence of recognition errors. Word-to-meaning associations are then used to instruct the robot to perform tasks and also allow to incorporate context in the speech recognition task.
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| 3. |
- Metta, Giorgio, et al.
(författare)
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The iCub humanoid robot : An open-systems platform for research in cognitive development
- 2010
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Ingår i: Neural Networks. - : Elsevier BV. - 0893-6080 .- 1879-2782. ; 23:8-9, s. 1125-1134
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Tidskriftsartikel (refereegranskat)abstract
- We describe a humanoid robot platform - the iCub - which was designed to support collaborative research in cognitive development through autonomous exploration and social interaction. The motivation for this effort is the conviction that significantly greater impact can be leveraged by adopting an open systems policy for software and hardware development. This creates the need for a robust humanoid robot that offers rich perceptuo-motor capabilities with many degrees of freedom, a cognitive capacity for learning and development, a software architecture that encourages reuse & easy integration, and a support infrastructure that fosters collaboration and sharing of resources. The iCub satisfies all of these needs in the guise of an open-system platform which is freely available and which has attracted a growing community of users and developers. To date, twenty iCubs each comprising approximately 5000 mechanical and electrical parts have been delivered to several research labs in Europe and to one in the USA.
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| 4. |
- Salvi, Giampiero, et al.
(författare)
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Language bootstrapping : Learning Word Meanings From Perception-Action Association
- 2012
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Ingår i: IEEE transactions on systems, man and cybernetics. Part B. Cybernetics. - 1083-4419 .- 1941-0492. ; 42:3, s. 660-671
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Tidskriftsartikel (refereegranskat)abstract
- We address the problem of bootstrapping language acquisition for an artificial system similarly to what is observed in experiments with human infants. Our method works by associating meanings to words in manipulation tasks, as a robot interacts with objects and listens to verbal descriptions of the interactions. The model is based on an affordance network, i.e., a mapping between robot actions, robot perceptions and the perceived effects of these actions upon objects. We extend the affordance model to incorporate spoken words, which allows us to ground the verbal symbols to the execution of actions and the perception of the environment.The model takes verbal descriptions of a task as the input, and uses temporal co-occurrence to create links between speech utterances and the involved objects, actions and effects. We show that the robot is able form useful word-to-meaning associations, even without considering grammatical structure in the learning process and in the presence of recognition errors. These word-to-meaning associations are embedded in the robot’s own understanding of its actions. Thus, they can be directly used to instruct the robot to perform tasks and also allow to incorporate context in the speech recognition task. We believe that the encouraging results with our approach may afford robots with a capacity to acquire language descriptors in their operation's environment as well as to shed some light as to how this challenging process develops with human infants.
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