论文标题
量角2.0：用于形状和力重建的光学触觉传感器
DenseTact 2.0: Optical Tactile Sensor for Shape and Force Reconstruction
论文作者
论文摘要
协作机器人将对国内服务应用中的人类福利产生巨大影响，并在高级制造中使用灵巧的组装产生巨大影响。出色的挑战是提供机器人指尖的物理设计，使他们擅长执行需要高分辨率，校准形状重建和力传感的灵活任务。在这项工作中，我们提出了Densetact 2.0，这是一种能够可视化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形状重建和6轴扳手估计的光学传感器。我们证明了用于形状重建的每个像素的传感器精度为0.3633毫米，力量为0.410N，扭矩为0.387nmm，以及通过传输学习来校准新手指的能力，这仅能实现可比性的性能，只有12％的非转移学习数据集尺寸。
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.
