Universal slip detection of robotic hand with tactile sensing
Slip detection is to recognize whether an object remains stable during grasping, which can significantly enhance manipulation dexterity. In this study, we explore slip detection for five-finger robotic hands being capable of performing various grasp types, and detect slippage across all five fingers...
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| Main Authors: | , , , , , |
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| Format: | Article |
| Language: | English |
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Frontiers Media S.A.
2025-02-01
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| Series: | Frontiers in Neurorobotics |
| Subjects: | |
| Online Access: | https://www.frontiersin.org/articles/10.3389/fnbot.2025.1478758/full |
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| _version_ | 1825206195788447744 |
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| author | Chuangri Zhao Yang Yu Zeqi Ye Ziyang Tian Yifan Zhang Ling-Li Zeng |
| author_facet | Chuangri Zhao Yang Yu Zeqi Ye Ziyang Tian Yifan Zhang Ling-Li Zeng |
| author_sort | Chuangri Zhao |
| collection | DOAJ |
| description | Slip detection is to recognize whether an object remains stable during grasping, which can significantly enhance manipulation dexterity. In this study, we explore slip detection for five-finger robotic hands being capable of performing various grasp types, and detect slippage across all five fingers as a whole rather than concentrating on individual fingertips. First, we constructed a dataset collected during the grasping of common objects from daily life across six grasp types, comprising more than 200 k data points. Second, according to the principle of deep double descent, we designed a lightweight universal slip detection convolutional network for different grasp types (USDConvNet-DG) to classify grasp states (no-touch, slipping, and stable grasp). By combining frequency with time domain features, the network achieves a computation time of only 1.26 ms and an average accuracy of over 97% on both the validation and test datasets, demonstrating strong generalization capabilities. Furthermore, we validated the proposed USDConvNet-DG in real-time grasp force adjustment in real-world scenarios, showing that it can effectively improve the stability and reliability of robotic manipulation. |
| format | Article |
| id | doaj-art-e0d65042a7444686a0429aaf67f0f79f |
| institution | Kabale University |
| issn | 1662-5218 |
| language | English |
| publishDate | 2025-02-01 |
| publisher | Frontiers Media S.A. |
| record_format | Article |
| series | Frontiers in Neurorobotics |
| spelling | doaj-art-e0d65042a7444686a0429aaf67f0f79f2025-02-07T13:14:36ZengFrontiers Media S.A.Frontiers in Neurorobotics1662-52182025-02-011910.3389/fnbot.2025.14787581478758Universal slip detection of robotic hand with tactile sensingChuangri ZhaoYang YuZeqi YeZiyang TianYifan ZhangLing-Li ZengSlip detection is to recognize whether an object remains stable during grasping, which can significantly enhance manipulation dexterity. In this study, we explore slip detection for five-finger robotic hands being capable of performing various grasp types, and detect slippage across all five fingers as a whole rather than concentrating on individual fingertips. First, we constructed a dataset collected during the grasping of common objects from daily life across six grasp types, comprising more than 200 k data points. Second, according to the principle of deep double descent, we designed a lightweight universal slip detection convolutional network for different grasp types (USDConvNet-DG) to classify grasp states (no-touch, slipping, and stable grasp). By combining frequency with time domain features, the network achieves a computation time of only 1.26 ms and an average accuracy of over 97% on both the validation and test datasets, demonstrating strong generalization capabilities. Furthermore, we validated the proposed USDConvNet-DG in real-time grasp force adjustment in real-world scenarios, showing that it can effectively improve the stability and reliability of robotic manipulation.https://www.frontiersin.org/articles/10.3389/fnbot.2025.1478758/fullslip detectionfive-finger robotic handdeep learning3-axial force tactile sensorgrasp types |
| spellingShingle | Chuangri Zhao Yang Yu Zeqi Ye Ziyang Tian Yifan Zhang Ling-Li Zeng Universal slip detection of robotic hand with tactile sensing Frontiers in Neurorobotics slip detection five-finger robotic hand deep learning 3-axial force tactile sensor grasp types |
| title | Universal slip detection of robotic hand with tactile sensing |
| title_full | Universal slip detection of robotic hand with tactile sensing |
| title_fullStr | Universal slip detection of robotic hand with tactile sensing |
| title_full_unstemmed | Universal slip detection of robotic hand with tactile sensing |
| title_short | Universal slip detection of robotic hand with tactile sensing |
| title_sort | universal slip detection of robotic hand with tactile sensing |
| topic | slip detection five-finger robotic hand deep learning 3-axial force tactile sensor grasp types |
| url | https://www.frontiersin.org/articles/10.3389/fnbot.2025.1478758/full |
| work_keys_str_mv | AT chuangrizhao universalslipdetectionofrobotichandwithtactilesensing AT yangyu universalslipdetectionofrobotichandwithtactilesensing AT zeqiye universalslipdetectionofrobotichandwithtactilesensing AT ziyangtian universalslipdetectionofrobotichandwithtactilesensing AT yifanzhang universalslipdetectionofrobotichandwithtactilesensing AT linglizeng universalslipdetectionofrobotichandwithtactilesensing |