  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.
