Standard robot grippers are not designed for material recognition. 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：标准机器人夹爪通常不适用于材料识别。我们通过实验评估了两种标准平行夹爪配合安装在机器人腕部的力/力矩传感器，在物体压缩过程中估算材料属性的精度，并以专业双轴压缩设备作为参考基准。在获取夹爪力-位移曲线后，将其转换为应力-应变曲线。我们分别在不同应变点估算弹性模量，并研究了多次压缩循环（预循环）、压缩速度及夹爪接触面积对估算结果的影响。通过压缩/解压缩循环的能量吸收、Kelvin-Voigt模型及Hunt-Crossley模型对粘弹性进行估算。研究发现：(1) 较慢的压缩速度可改善弹性估计效果，而预循环与接触面积影响不显著；(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精确估算能力上仍存在局限；(3) 不同夹爪对材料特性的相对排序具有较高一致性；(4) 尽管可变形物体呈现非线性特征，采用线性应力/应变近似拟合比局部杨氏模量估算能获得更稳定的结果；(5) Hunt-Crossley模型在单次压缩中表现最佳。通过单次抓取获得的弹性与粘弹性二维空间参数，有利于区分物体材料属性。我们在模拟单流回收场景中验证了该方法的应用效果——即使在不同位置压缩物体，塑料、纸张和金属物体均可通过单次抓取实现准确分离。相关数据与代码已公开共享。