A comprehensive review of robot intelligent grasping based on tactile perception

The Advancements in tactile sensors and machine learning techniques open new opportunities for achieving intelligent grasping in robotics. Traditional robot is limited in its ability to perform autonomous grasping in unstructured environments. Although the existing robotic grasping method enhances t...

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Vydané v:Robotics and computer-integrated manufacturing Ročník 90; s. 102792
Hlavní autori: Li, Tong, Yan, Yuhang, Yu, Chengshun, An, Jing, Wang, Yifan, Chen, Gang
Médium: Journal Article
Jazyk:English
Vydavateľské údaje: Elsevier Ltd 01.12.2024
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ISSN:0736-5845
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Abstract The Advancements in tactile sensors and machine learning techniques open new opportunities for achieving intelligent grasping in robotics. Traditional robot is limited in its ability to perform autonomous grasping in unstructured environments. Although the existing robotic grasping method enhances the robot's understanding of its environment by incorporating visual perception, it still lacks the capability for force perception and force adaptation. Therefore, tactile sensors are integrated into robot hands to enhance the robot's adaptive grasping capabilities in various complex scenarios by tactile perception. This paper primarily discusses the adaption of different types of tactile sensors in robotic grasping operations and grasping algorithms based on them. By dividing robotic grasping operations into four stages: grasping generation, robot planning, grasping state discrimination, and grasping destabilization adjustment, a further review of tactile-based and tactile-visual fusion methods is applied in related stages. The characteristics of these methods are comprehensively compared with different dimensions and indicators. Additionally, the challenges encountered in robotic tactile perception is summarized and insights into potential directions for future research are offered. This review is aimed for offering researchers and engineers a comprehensive understanding of the application of tactile perception techniques in robotic grasping operations, as well as facilitating future work to further enhance the intelligence of robotic grasping.
AbstractList The Advancements in tactile sensors and machine learning techniques open new opportunities for achieving intelligent grasping in robotics. Traditional robot is limited in its ability to perform autonomous grasping in unstructured environments. Although the existing robotic grasping method enhances the robot's understanding of its environment by incorporating visual perception, it still lacks the capability for force perception and force adaptation. Therefore, tactile sensors are integrated into robot hands to enhance the robot's adaptive grasping capabilities in various complex scenarios by tactile perception. This paper primarily discusses the adaption of different types of tactile sensors in robotic grasping operations and grasping algorithms based on them. By dividing robotic grasping operations into four stages: grasping generation, robot planning, grasping state discrimination, and grasping destabilization adjustment, a further review of tactile-based and tactile-visual fusion methods is applied in related stages. The characteristics of these methods are comprehensively compared with different dimensions and indicators. Additionally, the challenges encountered in robotic tactile perception is summarized and insights into potential directions for future research are offered. This review is aimed for offering researchers and engineers a comprehensive understanding of the application of tactile perception techniques in robotic grasping operations, as well as facilitating future work to further enhance the intelligence of robotic grasping.
ArticleNumber 102792
Author Yan, Yuhang
Li, Tong
An, Jing
Chen, Gang
Yu, Chengshun
Wang, Yifan
Author_xml – sequence: 1
  givenname: Tong
  orcidid: 0000-0001-7989-3355
  surname: Li
  fullname: Li, Tong
  email: tli@bupt.edu.cn
  organization: School of Modern Post (School of Automation), Beijing University of Posts and Telecommunications, Beijing 100876, China
– sequence: 2
  givenname: Yuhang
  surname: Yan
  fullname: Yan, Yuhang
  organization: School of Modern Post (School of Automation), Beijing University of Posts and Telecommunications, Beijing 100876, China
– sequence: 3
  givenname: Chengshun
  surname: Yu
  fullname: Yu, Chengshun
  organization: School of Modern Post (School of Automation), Beijing University of Posts and Telecommunications, Beijing 100876, China
– sequence: 4
  givenname: Jing
  surname: An
  fullname: An, Jing
  organization: School of Modern Post (School of Automation), Beijing University of Posts and Telecommunications, Beijing 100876, China
– sequence: 5
  givenname: Yifan
  surname: Wang
  fullname: Wang, Yifan
  organization: School of Artificial Intelligence, Beijing University of Posts and Telecommunications, Beijing 100876, China
– sequence: 6
  givenname: Gang
  surname: Chen
  fullname: Chen, Gang
  organization: School of Modern Post (School of Automation), Beijing University of Posts and Telecommunications, Beijing 100876, China
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ISSN 0736-5845
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Sat Nov 29 03:56:53 EST 2025
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Keywords Robotic grasping
Tactile sensor
Tactile perception
Tactile-visual fusion
Language English
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Snippet The Advancements in tactile sensors and machine learning techniques open new opportunities for achieving intelligent grasping in robotics. Traditional robot is...
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SubjectTerms Robotic grasping
Tactile perception
Tactile sensor
Tactile-visual fusion
Title A comprehensive review of robot intelligent grasping based on tactile perception
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