Learning to detect slip through tactile estimation of the contact force field and its entropy properties

Slip detection during object grasping and manipulation plays a vital role in object handling. Visual feedback can help devise a strategy for grasping. However, for robotic systems to attain a proficiency comparable to humans, integrating artificial tactile sensing is increasingly essential, especial...

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Bibliographic Details
Published inMechatronics (Oxford) Vol. 104; p. 103258
Main Authors Hu, Xiaohai, Venkatesh, Aparajit, Wan, Yusen, Zheng, Guiliang, Jawale, Neel, Kaur, Navneet, Chen, Xu, Birkmeyer, Paul
Format Journal Article
LanguageEnglish
Published Elsevier Ltd 01.12.2024
Subjects
Control system
Machine learning
Object manipulation
Slip detection
Tactile sensing
Tactile sensing
Object manipulation
Slip detection
Control system
Machine learning
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Summary:Slip detection during object grasping and manipulation plays a vital role in object handling. Visual feedback can help devise a strategy for grasping. However, for robotic systems to attain a proficiency comparable to humans, integrating artificial tactile sensing is increasingly essential, especially in consistently handling unfamiliar objects. We introduce a novel physics-informed, data-driven approach to detect slip continuously for control-oriented tasks. Our work leverages the inhomogeneity of tactile sensor readings during slip events to develop distinct features and formulates slip detection as a classification problem. We test multiple data-driven models on 10 common objects under different loading conditions, textures, and materials to evaluate our approach. The resulting best classification algorithm achieves a high average accuracy of 95.61%. Practical application in dynamic robotic manipulation demonstrates the effectiveness of the proposed real-time slip detection and prevention.
ISSN:0957-4158
DOI:10.1016/j.mechatronics.2024.103258