Camera-based tactile sensing is a low-cost, popular approach to obtain highly detailed contact geometry information. However, most existing camera-based tactile sensors are fingertip sensors, and longer fingers often require extraneous elements to obtain an extended sensing area similar to the full length of a human finger. Moreover, existing methods to estimate proprioceptive information such as total forces and torques applied on the finger from camera-based tactile sensors are not effective when the contact geometry is complex. We introduce GelSight Svelte, a curved, human finger-sized, single-camera tactile sensor that is capable of both tactile and proprioceptive sensing over a large area. GelSight Svelte uses curved mirrors to achieve the desired shape and sensing coverage. Proprioceptive information, such as the total bending and twisting torques applied on the finger, is reflected as deformations on the flexible backbone of GelSight Svelte, which are also captured by the camera. We train a convolutional neural network to estimate the bending and twisting torques from the captured images. We conduct gel deformation experiments at various locations of the finger to evaluate the tactile sensing capability and proprioceptive sensing accuracy. To demonstrate the capability and potential uses of GelSight Svelte, we conduct an object holding task with three different grasping modes that utilize different areas of the finger. More information is available on our website: https://gelsight-svelte.alanz.info

翻译：基于摄像头的触觉传感是一种低成本且广泛使用的方法，能够获取高分辨率的接触几何信息。然而，现有大多数基于摄像头的触觉传感器仅限于指尖感知，而更长的手指形状往往需要额外组件才能获得类似人类手指全长的大面积感知范围。此外，从基于摄像头的触觉传感器中估算施加在手指上的总力和力矩等本体感知信息时，现有方法在接触几何形状复杂的情况下效果不佳。我们提出GelSight Svelte，这是一种弯曲的、尺寸与人类手指相近的单摄像头触觉传感器，能够在较大面积上同时实现触觉感知与本体感知。GelSight Svelte利用曲面反射镜实现所需形状和感知覆盖范围。施加在手指上的总弯曲力矩和扭转力矩等本体感知信息，会在其柔性背板上表现为形变，这些形变同样被摄像头捕捉。我们训练一个卷积神经网络来从捕获的图像中估算弯曲力矩和扭转力矩。通过在手指不同位置进行凝胶形变实验，我们评估了触觉感知能力与本体感知精度。为展示GelSight Svelte的能力和潜在应用，我们使用三种不同抓取模式进行物体抓取任务，每种模式利用手指的不同区域。更多信息请访问我们的网站：https://gelsight-svelte.alanz.info