Grasping compliant objects is difficult for robots -- applying too little force may cause the grasp to fail, while too much force may lead to object damage. A robot needs to apply the right amount of force to quickly and confidently grasp the objects so that it can perform the required task. Although some methods have been proposed to tackle this issue, performance assessment is still a problem for directly measuring object property changes and possible damage. To fill the gap, a new concept is introduced in this paper to assess compliant robotic grasping using instrumented objects. A proof-of-concept design is proposed to measure the force applied on a cuboid object from a first-object perspective. The design can detect multiple contact locations and applied forces on its surface by using multiple embedded 3D Hall sensors to detect deformation relative to embedded magnets. The contact estimation is achieved by interpreting the Hall-effect signals using neural networks. In comprehensive experiments, the design achieved good performance in estimating contacts from each single face of the cuboid and decent performance in detecting contacts from multiple faces when being used to evaluate grasping from a parallel jaw gripper, demonstrating the effectiveness of the design and the feasibility of the concept.

翻译：抓取柔顺物体对机器人而言具有挑战性——施加力过小可能导致抓取失败，而力过大则可能损坏物体。机器人需要施加恰当大小的力，以快速且可靠地抓取物体，从而完成所需任务。尽管已有一些方法被提出以解决该问题，但在直接测量物体属性变化及潜在损伤方面的性能评估仍存在困难。为填补这一空白，本文引入了一种新概念，即利用仪器化物体评估柔顺机器人抓取。提出了一种概念验证设计，用于从物体第一视角测量施加在长方体物体上的力。该设计通过使用多个嵌入式3D霍尔传感器检测相对于嵌入磁体的变形，能够检测物体表面的多个接触位置及施加的力。通过利用神经网络解释霍尔效应信号，实现了接触估计。在综合实验中，该设计在估计长方体单个面的接触时表现良好，并在使用平行夹爪评估抓取时，对多个面的接触检测也取得了不错的效果，证明了设计的有效性及概念的可行性。