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

ABSTRACT Dexterous robotic hands are essential for various tasks in dynamic environments, but challenges such as slip detection and grasp stability affect real‐time performance. Traditional grasping methods often fail to detect subtle slip events, leading to unstable grasps. This paper proposes a real‐time slip detection and force compensation system using a hybrid convolutional neural networks and long short‐term memory (CNN‐LSTM) architecture to detect slip to enhance grasp stability. The system combines tactile sensing with deep learning to detect slips and dynamically adjust individual finger grasping forces, ensuring precise and stable object grasping. The proposed system leverages a hybrid CNN‐LSTM architecture to effectively capture both spatial and temporal features of slip dynamics, enabling robust slip detection and grasp stabilisation. By employing data augmentation techniques, the system generates a comprehensive dataset from limited experimental data, enhancing training efficiency and model generalisation. The approach extends slip detection to individual fingers, allowing real‐time monitoring and targeted force compensation when a slip is detected on a specific finger. This ensures adaptive and stable grasping, even in dynamic environments. Experimental results demonstrate significant improvements, with the CNN‐LSTM model achieving an 82% grasp success rate, outperforming traditional CNN (70%), LSTM (72%), and only traditional proportional–integral–derivative PID (54%) methods. The system's real‐time force adjustment capability prevents object drops and enhances overall grasp stability, making it highly scalable for applications in industrial automation, healthcare, and service robots. Despite the CNN‐LSTM architecture being a well‐established approach, it demonstrates exceptional performance in this task, achieving high accuracy and robustness in slip detection and grasp stabilisation.