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

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

URL: http://arxiv.org/abs/2303.00935v4
Date: Sun, 28 Apr 2024 17:29:03 GMT
Title: Learning to Detect Slip through Tactile Estimation of the Contact Force Field and its Entropy
Authors: Xiaohai Hu, Aparajit Venkatesh, Yusen Wan, Guiliang Zheng, Neel Jawale, Navneet Kaur, Xu Chen, Paul Birkmeyer,
Abstract summary: We introduce a 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 results show that the best classification algorithm achieves a high average accuracy of 95.61%.
Score: 6.739132519488627
License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
Abstract: 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.

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