Standard
Adaptive control of modular robots. / Demin, Alexander V.; Vityaev, Evgenii E.
Biologically Inspired Cognitive Architectures (BICA) for Young Scientists - Proceedings of the 1st International Early Research Career Enhancement School on BICA and Cybersecurity, FIERCES 2017. ed. / Alexei V. Samsonovich; Alexei V. Samsonovich; Valentin V. Klimov. Springer Nature, 2018. p. 205-212 (Advances in Intelligent Systems and Computing; Vol. 636).
Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › Research › peer-review
Harvard
Demin, AV
& Vityaev, EE 2018,
Adaptive control of modular robots. in AV Samsonovich, AV Samsonovich & VV Klimov (eds),
Biologically Inspired Cognitive Architectures (BICA) for Young Scientists - Proceedings of the 1st International Early Research Career Enhancement School on BICA and Cybersecurity, FIERCES 2017. Advances in Intelligent Systems and Computing, vol. 636, Springer Nature, pp. 205-212, 1st International Early Research Career Enhancement School on Biologically Inspired Cognitive Architectures, FIERCES on BICA 2017, Moscow, Russian Federation,
01.08.2017.
https://doi.org/10.1007/978-3-319-63940-6_29APA
Vancouver
Demin AV
, Vityaev EE.
Adaptive control of modular robots. In Samsonovich AV, Samsonovich AV, Klimov VV, editors, Biologically Inspired Cognitive Architectures (BICA) for Young Scientists - Proceedings of the 1st International Early Research Career Enhancement School on BICA and Cybersecurity, FIERCES 2017. Springer Nature. 2018. p. 205-212. (Advances in Intelligent Systems and Computing). doi: 10.1007/978-3-319-63940-6_29
Author
Demin, Alexander V.
; Vityaev, Evgenii E. /
Adaptive control of modular robots. Biologically Inspired Cognitive Architectures (BICA) for Young Scientists - Proceedings of the 1st International Early Research Career Enhancement School on BICA and Cybersecurity, FIERCES 2017. editor / Alexei V. Samsonovich ; Alexei V. Samsonovich ; Valentin V. Klimov. Springer Nature, 2018. pp. 205-212 (Advances in Intelligent Systems and Computing).
BibTeX
@inproceedings{1cfd240a792949afb53085b73da8c9a2,
title = "Adaptive control of modular robots",
abstract = "This paper proposes a learning control system of modular systems with a large number of degrees of freedom based on joint learning of modules, starting with finding the common control rules for all modules and finishing with their subsequent specification in accordance with the ideas of the semantic probabilistic inference. With an interactive 3D simulator, a number of successful experiments were carried out to train three robot models: snake-like robot, multiped robot and trunk-like robot. Pilot studies have shown that the approach proposed is quite effective and can be used to control the complex modular systems with many degrees of freedom.",
keywords = "Control system, Knowledge elicitation, Patterns detection",
author = "Demin, {Alexander V.} and Vityaev, {Evgenii E.}",
year = "2018",
doi = "10.1007/978-3-319-63940-6_29",
language = "English",
isbn = "9783319639390",
series = "Advances in Intelligent Systems and Computing",
publisher = "Springer Nature",
pages = "205--212",
editor = "Samsonovich, {Alexei V.} and Samsonovich, {Alexei V.} and Klimov, {Valentin V.}",
booktitle = "Biologically Inspired Cognitive Architectures (BICA) for Young Scientists - Proceedings of the 1st International Early Research Career Enhancement School on BICA and Cybersecurity, FIERCES 2017",
address = "United States",
note = "1st International Early Research Career Enhancement School on Biologically Inspired Cognitive Architectures, FIERCES on BICA 2017 ; Conference date: 01-08-2017 Through 06-08-2017",
}
RIS
TY - GEN
T1 - Adaptive control of modular robots
AU - Demin, Alexander V.
AU - Vityaev, Evgenii E.
PY - 2018
Y1 - 2018
N2 - This paper proposes a learning control system of modular systems with a large number of degrees of freedom based on joint learning of modules, starting with finding the common control rules for all modules and finishing with their subsequent specification in accordance with the ideas of the semantic probabilistic inference. With an interactive 3D simulator, a number of successful experiments were carried out to train three robot models: snake-like robot, multiped robot and trunk-like robot. Pilot studies have shown that the approach proposed is quite effective and can be used to control the complex modular systems with many degrees of freedom.
AB - This paper proposes a learning control system of modular systems with a large number of degrees of freedom based on joint learning of modules, starting with finding the common control rules for all modules and finishing with their subsequent specification in accordance with the ideas of the semantic probabilistic inference. With an interactive 3D simulator, a number of successful experiments were carried out to train three robot models: snake-like robot, multiped robot and trunk-like robot. Pilot studies have shown that the approach proposed is quite effective and can be used to control the complex modular systems with many degrees of freedom.
KW - Control system
KW - Knowledge elicitation
KW - Patterns detection
UR - http://www.scopus.com/inward/record.url?scp=85076351532&partnerID=8YFLogxK
U2 - 10.1007/978-3-319-63940-6_29
DO - 10.1007/978-3-319-63940-6_29
M3 - Conference contribution
AN - SCOPUS:85076351532
SN - 9783319639390
T3 - Advances in Intelligent Systems and Computing
SP - 205
EP - 212
BT - Biologically Inspired Cognitive Architectures (BICA) for Young Scientists - Proceedings of the 1st International Early Research Career Enhancement School on BICA and Cybersecurity, FIERCES 2017
A2 - Samsonovich, Alexei V.
A2 - Samsonovich, Alexei V.
A2 - Klimov, Valentin V.
PB - Springer Nature
T2 - 1st International Early Research Career Enhancement School on Biologically Inspired Cognitive Architectures, FIERCES on BICA 2017
Y2 - 1 August 2017 through 6 August 2017
ER -
