DenseTact 2.0: Optical Tactile Sensor for Shape and Force Reconstruction
DenseTact 2.0: Optical Tactile Sensor for Shape and Force Reconstruction
复制标题
DOI：
10.1109/icra48891.2023.10161150
复制
发表时间：
2022-09
期刊：
影响因子：
--
通讯作者：
Won Kyung Do;Bianca Jurewicz;Monroe Kennedy
中科院分区：
文献类型：
--
作者：
Won Kyung Do;Bianca Jurewicz;Monroe Kennedy
协作机器人将对家庭服务应用中的人类福利和具有灵巧装配的先进制造业中的工业优势产生巨大影响。突出的挑战是提供具有物理设计的机器人指尖，使它们能够执行需要高分辨率，校准形状重建和力传感的灵巧任务。在这项工作中，我们提出了DenseTact 2.0，一种光学触觉传感器，能够可视化柔软指尖的变形表面，并在神经网络中使用该图像来执行校准的形状重建和6轴扳手估计。我们证明了传感器的精度为0.3633毫米每像素的形状重建，0.410 N的力，0.387 N。mm的扭矩，以及通过迁移学习校准新手指的能力，它实现了与非迁移学习数据集大小的12%相当的性能。
Collaborative robots stand to have an immense impact on both human welfare in domestic service applications and industrial superiority in advanced manufacturing with dexterous assembly. The outstanding challenge is providing robotic fingertips with a physical design that makes them adept at performing dexterous tasks that require high-resolution, calibrated shape reconstruction and force sensing. In this work, we present DenseTact 2.0, an optical-tactile sensor capable of visualizing the deformed surface of a soft fingertip and using that image in a neural network to perform both calibrated shape reconstruction and 6-axis wrench estimation. We demon-strate the sensor accuracy of 0.3633mm per pixel for shape reconstruction, 0.410N for forces, 0.387N. mm for torques, and the ability to calibrate new fingers through transfer learning, which achieves comparable performance with only 12% of the non-transfer learning dataset size.