Bio-inspired control of dexterous manipulation

Robots successfully manipulate objects in controlled environments. However, they fail in unknown environments. Few years old children lift and manipulate unfamiliar objects more dexterously than today's robots. Therefore, roboticists are looking for inspiration on neurophysiological studies to...

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Detalles Bibliográficos
Autor principal: Matuk Herrera, Rosana Isabel
Publicado: 2005
Materias:
Dexterous manipulation
Neural networks
Reinforcement learning
Robotics
Algorithms
Artificial intelligence
Computer control
Force control
Learning systems
Manipulators
Neurophysiology
Robots
Artificial intelligence control model
Human manipulation process
Biocontrol
Acceso en línea:https://bibliotecadigital.exactas.uba.ar/collection/paper/document/paper_03029743_v3809LNAI_n_p1319_Herrera
http://hdl.handle.net/20.500.12110/paper_03029743_v3809LNAI_n_p1319_Herrera
Aporte de:
Biblioteca Digital - Facultad de Ciencias Exactas y Naturales (UBA) de Universidad de Buenos Aires
id paper:paper_03029743_v3809LNAI_n_p1319_Herrera
record_format dspace
spelling paper:paper_03029743_v3809LNAI_n_p1319_Herrera2023-06-08T15:28:23Z Bio-inspired control of dexterous manipulation Matuk Herrera, Rosana Isabel Dexterous manipulation Neural networks Reinforcement learning Robotics Algorithms Artificial intelligence Computer control Force control Learning systems Manipulators Neural networks Neurophysiology Robotics Robots Artificial intelligence control model Dexterous manipulation Human manipulation process Reinforcement learning Biocontrol Robots successfully manipulate objects in controlled environments. However, they fail in unknown environments. Few years old children lift and manipulate unfamiliar objects more dexterously than today's robots. Therefore, roboticists are looking for inspiration on neurophysiological studies to improve their robotics control models. We present an artificial intelligence control model for dexterous manipulation, and a grip and load force control algorithm, strongly inspired on neurophysiological studies of the human manipulation process. © Springer-Verlag Berlin Heidelberg 2005. Fil:Herrera, R.M. Universidad de Buenos Aires. Facultad de Ciencias Exactas y Naturales; Argentina. 2005 https://bibliotecadigital.exactas.uba.ar/collection/paper/document/paper_03029743_v3809LNAI_n_p1319_Herrera http://hdl.handle.net/20.500.12110/paper_03029743_v3809LNAI_n_p1319_Herrera
institution Universidad de Buenos Aires
institution_str I-28
repository_str R-134
collection Biblioteca Digital - Facultad de Ciencias Exactas y Naturales (UBA)
topic Dexterous manipulation
Neural networks
Reinforcement learning
Robotics
Algorithms
Artificial intelligence
Computer control
Force control
Learning systems
Manipulators
Neural networks
Neurophysiology
Robotics
Robots
Artificial intelligence control model
Dexterous manipulation
Human manipulation process
Reinforcement learning
Biocontrol
spellingShingle Dexterous manipulation
Neural networks
Reinforcement learning
Robotics
Algorithms
Artificial intelligence
Computer control
Force control
Learning systems
Manipulators
Neural networks
Neurophysiology
Robotics
Robots
Artificial intelligence control model
Dexterous manipulation
Human manipulation process
Reinforcement learning
Biocontrol
Matuk Herrera, Rosana Isabel
Bio-inspired control of dexterous manipulation
topic_facet Dexterous manipulation
Neural networks
Reinforcement learning
Robotics
Algorithms
Artificial intelligence
Computer control
Force control
Learning systems
Manipulators
Neural networks
Neurophysiology
Robotics
Robots
Artificial intelligence control model
Dexterous manipulation
Human manipulation process
Reinforcement learning
Biocontrol
description Robots successfully manipulate objects in controlled environments. However, they fail in unknown environments. Few years old children lift and manipulate unfamiliar objects more dexterously than today's robots. Therefore, roboticists are looking for inspiration on neurophysiological studies to improve their robotics control models. We present an artificial intelligence control model for dexterous manipulation, and a grip and load force control algorithm, strongly inspired on neurophysiological studies of the human manipulation process. © Springer-Verlag Berlin Heidelberg 2005.
author Matuk Herrera, Rosana Isabel
author_facet Matuk Herrera, Rosana Isabel
author_sort Matuk Herrera, Rosana Isabel
title Bio-inspired control of dexterous manipulation
title_short Bio-inspired control of dexterous manipulation
title_full Bio-inspired control of dexterous manipulation
title_fullStr Bio-inspired control of dexterous manipulation
title_full_unstemmed Bio-inspired control of dexterous manipulation
title_sort bio-inspired control of dexterous manipulation
publishDate 2005
url https://bibliotecadigital.exactas.uba.ar/collection/paper/document/paper_03029743_v3809LNAI_n_p1319_Herrera
http://hdl.handle.net/20.500.12110/paper_03029743_v3809LNAI_n_p1319_Herrera
work_keys_str_mv AT matukherrerarosanaisabel bioinspiredcontrolofdexterousmanipulation
_version_ 1768542596061200384

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