Sensorimotor representation learning for an "active self" in robots
- 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 aSafe 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.…
Author details: | Dong Hai Phuong NguyenORCiD, Yasmin Kim Georgie, Ezgi KayhanORCiDGND, Manfred EppeORCiDGND, Verena Vanessa HafnerORCiDGND, Stefan WermterORCiD |
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DOI: | https://doi.org/10.1007/s13218-021-00703-z |
ISSN: | 0933-1875 |
ISSN: | 1610-1987 |
Title of parent work (English): | Künstliche Intelligenz : KI ; Forschung, Entwicklung, Erfahrungen ; Organ des Fachbereichs 1 Künstliche Intelligenz der Gesellschaft für Informatik e.V., GI / Fachbereich 1 der Gesellschaft für Informatik e.V |
Subtitle (English): | a model survey |
Publisher: | Springer |
Place of publishing: | Berlin |
Publication type: | Article |
Language: | English |
Date of first publication: | 2021/02/18 |
Publication year: | 2021 |
Release date: | 2024/07/11 |
Tag: | Agency; Body schema; Developmental robotics; Peripersonal space; Robot learning |
Volume: | 35 |
Issue: | 1 |
Number of pages: | 27 |
First page: | 9 |
Last Page: | 35 |
Funding institution: | Projekt DEAL |
Organizational units: | Humanwissenschaftliche Fakultät / Strukturbereich Kognitionswissenschaften / Department Psychologie |
DDC classification: | 0 Informatik, Informationswissenschaft, allgemeine Werke / 00 Informatik, Wissen, Systeme / 004 Datenverarbeitung; Informatik |
Peer review: | Referiert |
Publishing method: | Open Access / Hybrid Open-Access |
License (German): | ![]() |