Slip Detection and Stable Grasping With Multi‐Fingered Robotic Hand Using Deep Learning Approach

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Real-time slip detection and force compensation enhances grasp stability in robotic hands, preventing object drops.
The hybrid model improves performance significantly with an 82% success rate, outpacing traditional methods.
Systematic use of tactile sensing allows dynamic adjustments for individual finger grasp forces during slip events.
Applications in healthcare and industrial automation highlight the versatility of the robotic hand technology.

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This approach demonstrates improved slip detection in robotic hands with deep learning, indicating potential in healthcare and industrial automation.

Xu et al. (2025) studied this question.

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This approach demonstrates improved slip detection in robotic hands with deep learning, indicating potential in healthcare and industrial automation.