Dynamic Layer Detection of a Thin Silk Cloth using DenseTact Optical Tactile Sensors
<|reference_start|>Dynamic Layer Detection of a Thin Silk Cloth using DenseTact Optical Tactile Sensors: 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 https://armlabstanford.github.io/dynamic-cloth-detection.<|reference_end|>
@article{dhawan2024dynamic,
    title={Dynamic Layer Detection of a Thin Silk Cloth using DenseTact Optical
  Tactile Sensors},
    author={Ankush Kundan Dhawan and Camille Chungyoun and Karina Ting and Monroe
  Kennedy III},
    journal={arXiv preprint arXiv:2409.09849},
    year={2024},
    archivePrefix={arXiv},
    eprint={2409.09849},
    primaryClass={cs.RO}
}