A Tactile Rubbing Gripper for Reliable Fabric Separation
Zhengrong LING, Zhenghao HUANG, Yajing Shen
AI summary
Problem
Existing robotic grippers struggle with reliable single-layer fabric separation due to passive mechanisms, environmental sensitivity, and lack of real-time separation state sensing.
Approach
The system actively separates fabric layers through rotational rubbing and uses a magnetic tactile sensing array with a neural network to detect sliding interfaces in real time.
Key results
- 96.67% separation success rate across 15 fabrics
- 87.00% tactile sliding detection accuracy
- Robust performance across varying layer counts and separation positions
- Automated gripping-feedback-correction pipeline for reliable isolation
Why it matters
Enables reliable automation of a fundamental textile handling task, advancing robotic fabric manipulation for manufacturing and domestic applications.
Abstract
Automated fabric manipulation offers great po- tential for reducing labor requirements in textile manufac- turing and domestic services. Yet, even the basic task of separating a single fabric layer poses substantial challenges for robots. Adhesive-based end-effectors suffer from limited material compatibility and environmental adaptability, while gripper-based designs, which primarily target crease grasping and rely on passive separation, frequently demonstrate unre- liability. Current vision and tactile systems fail to detect the fabric separation surface. Given these mechanical and sensing constraints, existing separation solutions lack the ability to adjust the number of layers post-grasping, relying solely on single-attempt success. In this work, we propose a novel tactile- enhanced gripper capable of human-like rubbing motion for reliable cloth separation, which integrates a magnetic sensing system to monitor the separation process. Based on these, we further develop a pipeline to realize rubbing-based separation. Extensive experiments show our gripper achieves a 96.67% separation success rate across 15 fabrics with varying weaving patterns, and the tactile system reaches 87.00% accuracy in sliding surface detection. Our work provides a novel mecha- nism for fabric layer separation, facilitating subsequent cloth manipulation.