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.

翻译：我们通过实验评估了两种标准平行夹爪配合腕部力/力矩传感器进行物体压缩时材料属性估计的精度，并以专业双轴压缩设备作为参考基准。获取了夹爪力-位移曲线并将其转化为应力-应变曲线，在不同应变点估计弹性模量，并研究了多次压缩循环（预循环）、压缩速度及夹爪表面积对估计结果的影响。利用压缩/解压缩循环中的能量吸收、Kelvin-Voigt模型及Hunt-Crossley模型进行粘弹性估计。研究发现：(1)较慢的压缩速度可改善弹性估计，而预循环或表面积无显著影响；(2)即使经过校准，机器人夹爪在杨氏模量和粘弹性的绝对值估计精度方面仍存在局限性；(3)不同夹爪间的材料特性相对排序保持高度一致；(4)尽管可变形物体具有非线性特征，线性应力/应变近似拟合比局部杨氏模量估计能获得更稳定的结果；(5)Hunt-Crossley模型在单次物体压缩下对粘弹性估计效果最佳。由单次抓取获得的弹性与粘弹性估计构成的二维空间有利于区分物体材料属性。我们在模拟单一流回收场景中验证了研究成果的应用价值：即使在不同位置压缩物体，塑料、纸张和金属物体仍可通过单次抓取实现正确分拣。相关数据与代码已公开提供。