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：抓取易变形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，而施加力过大则可能造成物体损坏。机器人需要施加适当大小的力，以快速可靠地抓取物体，从而完成预定任务。尽管已有部分方法被提出用于解决该问题，但在直接测量物体属性变化及潜在损伤方面，性能评估仍存在困难。为填补这一空白，本文提出了一种新概念，利用仪器化物体评估合规机器人抓取。我们设计了一个概念验证系统，从物体自身角度测量作用于立方体表面的抓取力。该设计通过嵌入多个3D霍尔传感器检测相对嵌入式磁铁的形变，从而感知表面上的多个接触位置及施加的力。接触估计通过神经网络解读霍尔效应信号实现。在综合实验中，该设计在估计立方体各单面接触时取得了良好性能，并在使用平行爪夹持器评估抓取时，对多面接触的检测表现亦佳，验证了设计的有效性与概念的可行性。