Learning to Detect Slip through Tactile Estimation of the Contact Force Field and its Entropy

Detection of slip during object grasping and manipulation plays a vital role in object handling. Existing solutions primarily rely on visual information to devise a strategy for grasping. However, for robotic systems to attain a level of proficiency comparable to humans, especially in consistently h...

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Bibliographic Details
Published inarXiv.org
Main Authors Hu, Xiaohai, Venkatesh, Aparajit, Wan, Yusen, Zheng, Guiliang, Neel Jawale, Kaur, Navneet, Chen, Xu, Birkmeyer, Paul
Format Paper
LanguageEnglish
Published Ithaca Cornell University Library, arXiv.org 28.04.2024
Subjects
Algorithms
Classification
Contact force
Grasping
Grippers
Inhomogeneity
Real time
Slip
Tactile sensors (robotics)
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Summary:Detection of slip during object grasping and manipulation plays a vital role in object handling. Existing solutions primarily rely on visual information to devise a strategy for grasping. However, for robotic systems to attain a level of proficiency comparable to humans, especially in consistently handling and manipulating unfamiliar objects, integrating artificial tactile sensing is increasingly essential. We introduce a novel physics-informed, data-driven approach to detect slip continuously in real time. We employ the GelSight Mini, an optical tactile sensor, attached to custom-designed grippers to gather tactile data. Our work leverages the inhomogeneity of tactile sensor readings during slip events to develop distinctive features and formulates slip detection as a classification problem. To evaluate our approach, we test multiple data-driven models on 10 common objects under different loading conditions, textures, and materials. Our results show that the best classification algorithm achieves a high average accuracy of 95.61%. We further illustrate the practical application of our research in dynamic robotic manipulation tasks, where our real-time slip detection and prevention algorithm is implemented.
ISSN:2331-8422