Dynamic Layer Detection of a Thin Silk Cloth using DenseTact Optical Tactile Sensors
| Autor: | Dhawan, Ankush Kundan, Chungyoun, Camille, Ting, Karina, Kennedy III, Monroe |
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| Rok vydání: | 2024 |
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| Druh dokumentu: | Working Paper |
| Popis: | 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. Comment: 6 pages, 8 figures, submitted to ICRA 2025 |
| Databáze: | arXiv |
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