eTALK embodied Thought for Abstract Language Knowledge

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Language is the most natural means of communication among people, as well as for interaction betweenpeople and robots. As the great majority of words people use are abstract words, to achieve natural languageinteraction with robots it is crucial that machines can handle both concrete and abstract concepts. This timelyproject strategically builds on the recent, substantial advances in developmental psychology and embodiedcognition theories on abstract concepts, on developmental robotics and AI methods for cognitive modelling,and in human-robot interaction and language use, to bootstrap our scientific and technological understandingof the grounding of abstract concepts and words in robotic agents. The project will develop and test a novelcognitive developmental architecture utilising the latest generative deep learning models and developmentalrobotics methods for human-robot interaction experiments on the learning of concrete and abstract words viagrounding transfer. Three case studies have been strategically planned to show the breadth and robustness ofthe proposed approach, and to demonstrate the flexibility of the computational approach in dealing withvarious types of abstract concepts. The case studies focus on: (i) abstract word grounding for perceptual,motor and affective concepts, (ii) number word learning, and (iii) function words for demonstrative (deictic)prepositions, words about time and fuzzy quantifiers. This project will lead to breakthroughs in our deeperunderstanding of the computational approaches to the grounding and embodiment bases of language andcognition and in the design of humanoid robots which can learn to understand, and act on, conversationsabout a variety of abstract words, as these are intrinsically grounded in their own sensorimotor experience.The project will benefit from direct engagement with users and stakeholders of the application domains, aswell as from the international advisors from both academia and industry.