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A Survey of Multifingered Robotic Manipulation: Biological Results, Structural Evolvements, and Learning Methods
Multifingered robotic hands (usually referred to as dexterous hands) are designed to achieve human-level or human-like manipulations for robots or as prostheses for the disabled. The research dates back 30 years ago, yet, there remain great challenges to effectively design and control them due to th...
Autores principales: | , , , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
Frontiers Media S.A.
2022
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9097019/ https://www.ncbi.nlm.nih.gov/pubmed/35574228 http://dx.doi.org/10.3389/fnbot.2022.843267 |
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author | Li, Yinlin Wang, Peng Li, Rui Tao, Mo Liu, Zhiyong Qiao, Hong |
author_facet | Li, Yinlin Wang, Peng Li, Rui Tao, Mo Liu, Zhiyong Qiao, Hong |
author_sort | Li, Yinlin |
collection | PubMed |
description | Multifingered robotic hands (usually referred to as dexterous hands) are designed to achieve human-level or human-like manipulations for robots or as prostheses for the disabled. The research dates back 30 years ago, yet, there remain great challenges to effectively design and control them due to their high dimensionality of configuration, frequently switched interaction modes, and various task generalization requirements. This article aims to give a brief overview of multifingered robotic manipulation from three aspects: a) the biological results, b) the structural evolvements, and c) the learning methods, and discuss potential future directions. First, we investigate the structure and principle of hand-centered visual sensing, tactile sensing, and motor control and related behavioral results. Then, we review several typical multifingered dexterous hands from task scenarios, actuation mechanisms, and in-hand sensors points. Third, we report the recent progress of various learning-based multifingered manipulation methods, including but not limited to reinforcement learning, imitation learning, and other sub-class methods. The article concludes with open issues and our thoughts on future directions. |
format | Online Article Text |
id | pubmed-9097019 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2022 |
publisher | Frontiers Media S.A. |
record_format | MEDLINE/PubMed |
spelling | pubmed-90970192022-05-13 A Survey of Multifingered Robotic Manipulation: Biological Results, Structural Evolvements, and Learning Methods Li, Yinlin Wang, Peng Li, Rui Tao, Mo Liu, Zhiyong Qiao, Hong Front Neurorobot Neuroscience Multifingered robotic hands (usually referred to as dexterous hands) are designed to achieve human-level or human-like manipulations for robots or as prostheses for the disabled. The research dates back 30 years ago, yet, there remain great challenges to effectively design and control them due to their high dimensionality of configuration, frequently switched interaction modes, and various task generalization requirements. This article aims to give a brief overview of multifingered robotic manipulation from three aspects: a) the biological results, b) the structural evolvements, and c) the learning methods, and discuss potential future directions. First, we investigate the structure and principle of hand-centered visual sensing, tactile sensing, and motor control and related behavioral results. Then, we review several typical multifingered dexterous hands from task scenarios, actuation mechanisms, and in-hand sensors points. Third, we report the recent progress of various learning-based multifingered manipulation methods, including but not limited to reinforcement learning, imitation learning, and other sub-class methods. The article concludes with open issues and our thoughts on future directions. Frontiers Media S.A. 2022-04-27 /pmc/articles/PMC9097019/ /pubmed/35574228 http://dx.doi.org/10.3389/fnbot.2022.843267 Text en Copyright © 2022 Li, Wang, Li, Tao, Liu and Qiao. https://creativecommons.org/licenses/by/4.0/This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) and the copyright owner(s) are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms. |
spellingShingle | Neuroscience Li, Yinlin Wang, Peng Li, Rui Tao, Mo Liu, Zhiyong Qiao, Hong A Survey of Multifingered Robotic Manipulation: Biological Results, Structural Evolvements, and Learning Methods |
title | A Survey of Multifingered Robotic Manipulation: Biological Results, Structural Evolvements, and Learning Methods |
title_full | A Survey of Multifingered Robotic Manipulation: Biological Results, Structural Evolvements, and Learning Methods |
title_fullStr | A Survey of Multifingered Robotic Manipulation: Biological Results, Structural Evolvements, and Learning Methods |
title_full_unstemmed | A Survey of Multifingered Robotic Manipulation: Biological Results, Structural Evolvements, and Learning Methods |
title_short | A Survey of Multifingered Robotic Manipulation: Biological Results, Structural Evolvements, and Learning Methods |
title_sort | survey of multifingered robotic manipulation: biological results, structural evolvements, and learning methods |
topic | Neuroscience |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9097019/ https://www.ncbi.nlm.nih.gov/pubmed/35574228 http://dx.doi.org/10.3389/fnbot.2022.843267 |
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