  Fluid-filled soft visuotactile sensors such as the Soft-bubbles alleviate key
challenges for robust manipulation, as they enable reliable grasps along with
the ability to obtain high-resolution sensory feedback on contact geometry and
forces. Although they are simple in construction, their utility has been
limited due to size constraints introduced by enclosed custom IR/depth imaging
sensors to directly measure surface deformations. Towards mitigating this
limitation, we investigate the application of state-of-the-art monocular depth
estimation to infer dense internal (tactile) depth maps directly from the
internal single small IR imaging sensor. Through real-world experiments, we
show that deep networks typically used for long-range depth estimation (1-100m)
can be effectively trained for precise predictions at a much shorter range
(1-100mm) inside a mostly textureless deformable fluid-filled sensor. We
propose a simple supervised learning process to train an object-agnostic
network requiring less than 10 random poses in contact for less than 10 seconds
for a small set of diverse objects (mug, wine glass, box, and fingers in our
experiments). We show that our approach is sample-efficient, accurate, and
generalizes across different objects and sensor configurations unseen at
training time. Finally, we discuss the implications of our approach for the
design of soft visuotactile sensors and grippers.
