Paper ID: 2409.09849

Dynamic Layer Detection of a Thin Silk Cloth using DenseTact Optical Tactile Sensors

Ankush Kundan Dhawan, Camille Chungyoun, Karina Ting, Monroe Kennedy III

Cloth manipulation is an important aspect of many everyday tasks and remains a significant challenge for robots. While existing research has made strides in tasks like cloth smoothing and folding, many studies struggle with common failure modes (crumpled corners/edges, incorrect grasp configurations) that a preliminary step of cloth layer detection can solve. We present a novel method for classifying the number of grasped cloth layers using a custom gripper equipped with DenseTact 2.0 optical tactile sensors. After grasping a cloth, the gripper performs an anthropomorphic rubbing motion while collecting optical flow, 6-axis wrench, and joint state data. Using this data in a transformer-based network achieves a test accuracy of 98.21% in correctly classifying the number of grasped layers, showing the effectiveness of our dynamic rubbing method. Evaluating different inputs and model architectures highlights the usefulness of using tactile sensor information and a transformer model for this task. A comprehensive dataset of 368 labeled trials was collected and made open-source along with this paper. Our project page is available at this https URL.

Submitted: Sep 15, 2024