  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
