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  Sensorimotor representation learning for an “active self” in robots: A model survey

Nguyen, P. D. H., Georgie, Y. K., Kayhan, E., Eppe, M., Hafner, V. V., & Wermter, S. (2021). Sensorimotor representation learning for an “active self” in robots: A model survey. KI - Künstliche Intelligenz. doi:10.1007/s13218-021-00703-z.

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 Creators:
Nguyen, Phuong D. H.1, Author
Georgie, Yasmin Kim2, Author
Kayhan, Ezgi3, 4, Author              
Eppe, Manfred1, Author
Hafner, Verena Vanessa2, Author
Wermter, Stefan1, Author
Affiliations:
1Department of Informatics, University of Hamburg, Germany, ou_persistent22              
2Department of Computer Science, Humboldt University Berlin, Germany, ou_persistent22              
3Department of Developmental Psychology, University of Potsdam, Germany, ou_persistent22              
4Max Planck Research Group Early Social Cognition, MPI for Human Cognitive and Brain Sciences, Max Planck Society, ou_2355694              

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Free keywords: Developmental robotics; Body schema; Peripersonal space; Agency; Robot learning
 Abstract: Safe human-robot interactions require robots to be able to learn how to behave appropriately in spaces populated by people and thus to cope with the challenges posed by our dynamic and unstructured environment, rather than being provided a rigid set of rules for operations. In humans, these capabilities are thought to be related to our ability to perceive our body in space, sensing the location of our limbs during movement, being aware of other objects and agents, and controlling our body parts to interact with them intentionally. Toward the next generation of robots with bio-inspired capacities, in this paper, we first review the developmental processes of underlying mechanisms of these abilities: The sensory representations of body schema, peripersonal space, and the active self in humans. Second, we provide a survey of robotics models of these sensory representations and robotics models of the self; and we compare these models with the human counterparts. Finally, we analyze what is missing from these robotics models and propose a theoretical computational framework, which aims to allow the emergence of the sense of self in artificial agents by developing sensory representations through self-exploration.

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Language(s): eng - English
 Dates: 2020-06-122021-01-132021-02-18
 Publication Status: Published online
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 Rev. Type: -
 Identifiers: DOI: 10.1007/s13218-021-00703-z
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Project name : -
Grant ID : KA 4926/1-1, 402790442, 402776968, 433323019
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Funding organization : Deutsche Forschungsgemeinschaft (DFG)

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Title: KI - Künstliche Intelligenz
Source Genre: Journal
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Pages: - Volume / Issue: - Sequence Number: - Start / End Page: - Identifier: ISSN: 0933-1875
CoNE: https://pure.mpg.de/cone/journals/resource/110978979267292