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

Towards Assessing Compliant Robotic Grasping from First-Object Perspective via Instrumented Objects