  Compliance is a useful parametrization of tactile information that humans
often utilize in manipulation tasks. It can be used to inform low-level
contact-rich actions or characterize objects at a high-level. In robotic
manipulation, existing approaches to estimate compliance have struggled to
generalize across object shape and material. Using camera-based tactile
sensors, we present a novel approach to parametrize compliance through Young's
modulus E. We evaluate our method over a novel dataset of 285 common objects,
including a wide array of shapes and materials with Young's moduli ranging from
5.0 kPa to 250 GPa. Data is collected over automated parallel grasps of each
object. Combining analytical and data-driven approaches, we develop a hybrid
system using a multi-tower neural network to analyze a sequence of tactile
images from grasping. This system is shown to estimate the Young's modulus of
unseen objects within an order of magnitude at 74.2% accuracy across our
dataset. This is a drastic improvement over a purely analytical baseline, which
exhibits only 28.9% accuracy. Importantly, this estimation system performs
irrespective of object geometry and demonstrates robustness across object
materials. Thus, it could be applied in a general robotic manipulation setting
to characterize unknown objects and inform decision-making, for instance to
sort produce by ripeness.
