用触觉传感器动态检测抓取布料/纸张的层数，准确率达98%
Dynamic Layer Detection of Thin Materials using DenseTact Optical Tactile Sensors
- 抓取后通过仿人揉搓动作采集触觉与力数据
- 布料层数分类准确率98.21%，纸张81.25%
- 开源568组标注数据集，适合机器人抓取研究者
薄材料操作对机器人完成日常任务至关重要，但现有方法常因折角褶皱、抓握配置错误等失败。本文提出一种基于自研夹持器与DenseTact 2.0光学触觉传感器的新方法，通过抓取后执行仿人揉搓动作，同步采集光流、六维力矩和关节状态数据。利用Transformer网络进行层叠分类，在布料上达到98.21%的测试准确率，纸张为81.25%，验证了动态揉搓策略的有效性。对比不同输入与模型结构表明触觉信息与Transformer架构的关键作用。本文构建并开源包含368次布料与200次纸张实验的568组标注数据集。项目页面见https://armlabstanford.github.io/dynamic-cloth-detection。
原文摘要 · Abstract (English)
Manipulation of thin materials is critical for many everyday tasks and remains a significant challenge for robots. While existing research has made strides in tasks like material smoothing and folding, many studies struggle with common failure modes (crumpled corners/edges, incorrect grasp configurations) that a preliminary step of layer detection could solve. We present a novel method for classifying the number of grasped material layers using a custom gripper equipped with DenseTact 2.0 optical tactile sensors. After grasping, 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 classifying the number of grasped cloth layers, and 81.25\% accuracy in classifying layers of grasped paper, showing the effectiveness of our dynamic rubbing method. Evaluating different inputs and model architectures highlights the usefulness of tactile sensor information and a transformer model for this task. A comprehensive dataset of 568 labeled trials (368 for cloth and 200 for paper) was collected and made open-source along with this paper. Our project page is available at https://armlabstanford.github.io/dynamic-cloth-detection.
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