  We experimentally evaluated the accuracy with which material properties can
be estimated through object compression by two standard parallel jaw grippers
and a force/torque sensor mounted at the robot wrist, with a professional
biaxial compression device used as reference. Gripper effort versus position
curves were obtained and transformed into stress/strain curves. The modulus of
elasticity was estimated at different strain points and the effect of multiple
compression cycles (precycling), compression speed, and the gripper surface
area on estimation was studied. Viscoelasticity was estimated using the energy
absorbed in a compression/decompression cycle, the Kelvin-Voigt, and
Hunt-Crossley models. We found that: (1) slower compression speeds improved
elasticity estimation, while precycling or surface area did not; (2) the robot
grippers, even after calibration, were found to have a limited capability of
delivering accurate estimates of absolute values of Young's modulus and
viscoelasticity; (3) relative ordering of material characteristics was largely
consistent across different grippers; (4) despite the nonlinear characteristics
of deformable objects, fitting linear stress/strain approximations led to more
stable results than local estimates of Young's modulus; (5) the Hunt-Crossley
model worked best to estimate viscoelasticity, from a single object
compression. A two-dimensional space formed by elasticity and viscoelasticity
estimates obtained from a single grasp is advantageous for the discrimination
of the object material properties. We demonstrated the applicability of our
findings in a mock single stream recycling scenario, where plastic, paper, and
metal objects were correctly separated from a single grasp, even when
compressed at different locations on the object. The data and code are publicly
available.
