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 demonstrate the sensor accuracy of 0.3633mm per pixel for shape reconstruction, 0.410N for forces, 0.387Nmm 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.

翻译：协作机器人在家政服务应用中将对人类福祉产生巨大影响，并在精密装配等先进制造业中提升工业优势。当前的关键挑战在于为机器人指尖提供物理设计，使其能够胜任需要高分辨率、校准化形状重建与力感知的灵巧操作任务。本文提出DenseTact 2.0——一种光学触觉传感器，能够可视化软指端的变形表面，并利用该图像通过神经网络实现校准化形状重建与六轴力觉估计。实验表明，该传感器在形状重建中达到每像素0.3633毫米的精度，力检测精度为0.410牛顿，力矩检测精度为0.387牛顿毫米，并具备通过迁移学习校准新指尖的能力——仅需非迁移学习数据集12%的样本即可获得可比性能。