Language Acquisition and Symbol Grounding Transfer with Neural Networks and Cognitive Robots

Cangelosi, Angelo; Hourdakis, Emmanouil; Tikhanoff, Vadim

doi:10.1109/ijcnn.2006.246621

Cited by 19 publications

(11 citation statements)

References 24 publications

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“…However, ongoing research is focusing on the scaling up of this model. For example, Hourkadis and Cangelosi (2005; see also Cangelosi, Hourdakis, & Tikhanoff, 2006) have expanded the neural network controller of the robot to include both language production and comprehension capabilities. The neural network receives in input both visual information and language so that the agent can produce linguistic descriptions (vision input to language output) as well as be able to understand language (from language input to motor output).…”

Section: Discussionmentioning

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: Discussionmentioning

confidence: 99%

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

2006

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“…A similarly strong focus on words referring to concrete physical objects, processes, actions, and object properties such as color or size is prevalent in language-oriented research in developmental robotics [8]- [25] and agent-based approaches in evolutionary linguistics [26]- [29]. There, symbol grounding [30], the linking of symbols with data or concepts derived from the robot's own embodiment, is employed for robots to "make sense" of these linguistic entities.…”

Section: Introductionmentioning

confidence: 99%

Robots That Say “No” Affective Symbol Grounding and the Case of Intent Interpretations

Förster

Saunders

Nehaniv

2018

IEEE Trans. Cogn. Dev. Syst.

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Modern theories on early child language acquisition tend to focus on referential words, mostly nouns, labeling concrete objects, or physical properties. In this experimental proof-ofconcept study, we show how nonreferential negation words, typically belonging to a child's first ten words, may be acquired. A childlike humanoid robot is deployed in speech-wise unconstrained interaction with naïve human participants. In agreement with psycholinguistic observations, we corroborate the hypothesis that affect plays a pivotal role in the socially distributed acquisition process where the adept conversation partner provides linguistic interpretations of the affective displays of the less adept speaker. Negation words are prosodically salient within intent interpretations that are triggered by the learner's display of affect. From there they can be picked up and used by the budding language learner which may involve the grounding of these words in the very affective states that triggered them in the first place. The pragmatic analysis of the robot's linguistic performance indicates that the correct timing of negative utterances is essential for the listener to infer the meaning of otherwise ambiguous negative utterances. In order to assess the robot's performance thoroughly comparative data from psycholinguistic studies of parent-child dyads is needed highlighting the need for further interdisciplinary work.

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“…A vast array of methods can be used to realise the language acquisition task in humanoid robots. These range from Bayesian networks such as hidden Markov models [9] to artificial neural networks [10]. Due to the sequential nature of the data for the language task at hand, Recurrent Neural Networks (RNNs) are effective for fulfilling the task at hand.…”

Section: Recurrent Neural Network For Language Learningmentioning

confidence: 99%

Communication with Speech and Gestures: Applications of Recurrent Neural Networks to Robot Language Learning

Antunes¹,

Pizzuto²,

Cangelosi³

2017

GLU 2017 International Workshop on Grounding Language Understanding

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Recurrent neural networks have recently shown significant potential in different language applications, ranging from natural language processing to language modelling. This paper introduces a research effort to use such networks to develop and evaluate natural language acquisition on a humanoid robot. Here, the problem is twofold. First, the focus will be put on using the gesture-word combination stage observed in infants to transition from single to multi-word utterances. Secondly, research will be carried out in the domain of connecting action learning with language learning. In the former, the long-short term memory architecture will be implemented, whilst in the latter multiple time-scale recurrent neural networks will be used. This will allow for comparison between the two architectures, whilst highlighting the strengths and shortcomings of both with respect to the language learning problem. Here, the main research efforts, challenges and expected outcomes are described.

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Language Acquisition and Symbol Grounding Transfer with Neural Networks and Cognitive Robots

Cited by 19 publications

References 24 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

Robots That Say “No” Affective Symbol Grounding and the Case of Intent Interpretations

Communication with Speech and Gestures: Applications of Recurrent Neural Networks to Robot Language Learning

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