Standard robot grippers are not designed for elasticity estimation. In this work, a professional biaxial compression device was used as a control setup to study the accuracy with which material properties can be estimated by two standard parallel jaw grippers and a force/torque sensor mounted at the robot wrist. Using three sets of deformable objects, different parameters were varied to observe their effect on measuring material characteristics: (1) repeated compression cycles, (2) compression speed, and (3) the surface area of the gripper jaws. Gripper effort versus position curves were obtained and transformed into stress/strain curves. The modulus of elasticity was estimated at different strain points. Viscoelasticity was assessed using the energy absorbed in a compression/decompression cycle, the Kelvin-Voigt, and Hunt-Crossley models. Our results can be summarized as follows: (1) better results were obtained with slower compression speeds, while additional compression cycles or surface area did not improve estimation; (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) to assess viscoelasticity, the Hunt-Crossley model worked best. Finally, we show that a two-dimensional space representing elasticity and viscoelasticity estimates is advantageous for the discrimination of deformable objects. A single-grasp, online, classification and sorting of such objects is thus possible. An additional contribution is the dataset and data processing codes that we make publicly available.

翻译：标准机器人夹爪并非为弹性估计而设计。本研究采用专业双轴压缩装置作为对照组，探究通过两种标准平行夹爪及安装在机器人腕部的力/力矩传感器估算材料属性的精度。使用三组可变形物体，通过改变不同参数观测其对材料特性测量的影响：（1）重复压缩循环次数；（2）压缩速度；（3）夹爪接触面积。获取夹爪力与位置关系曲线，并将其转换为应力/应变曲线。在不同应变点估计弹性模量。利用压缩/解压缩循环中吸收的能量、Kelvin-Voigt模型及Hunt-Crossley模型评估粘弹性。研究结果可归纳如下：（1）较低压缩速度可获得更优结果，而增加压缩循环次数或接触面积未改善估计效果；（2）即使经过校准，机器人夹爪在提供杨氏模量和粘弹性的绝对值准确估计方面能力有限；（3）不同夹爪对材料特性相对排序的评估结果基本一致；（4）尽管可变形物体具有非线性特性，但采用线性应力/应变近似拟合比局部杨氏模量估计更能获得稳定结果；（5）在评估粘弹性方面，Hunt-Crossley模型表现最佳。最后，我们证明基于弹性和粘弹性估计构建的二维空间有利于可变形物体的判别，从而实现单次抓取在线分类与分拣。本研究的另一贡献在于公开了数据集与数据处理代码。