A multi wavelet transform sliding feature detection method is proposed to address the difficulty in identifying sliding feature signals during the process of robotic arm grasping targets. Firstly, the mechanism of flexible tactile slip sensing based on FBG sensing is studied, and a double-layer “cross” type distributed sensing unit based on FBG is designed. Secondly, a tactile perception experimental platform is established and tactile perception experiments are implemented on the dynamic grasping process. Then, based on the db10 wavelet denoising method, the sliding perception signal is denoised. Finally, a sliding signal feature separation and perception method using the Mexican hat continuous wavelet and the first-order Haar discrete wavelet is proposed, and relevant experimental research is conducted. The experimental results show that the detection threshold of wavelet detail coefficients is ±2×10 -4 , and the average accuracy of sliding detection with different grip forces can reach 98. 88% , which can accurately identify the sliding state of the target being grasped by the robotic arm.