通过仪器化物体从第一物体视角评估柔顺机器人抓取
Towards Assessing Compliant Robotic Grasping from First-Object Perspective via Instrumented Objects
AI总结：
本文提出利用仪器化物体从第一物体视角评估柔顺机器人抓取，通过嵌入式3D霍尔传感器和神经网络实现多接触位置与力的检测，实验验证了该概念的有效性与可行性。
摘要：
抓取柔顺物体对机器人而言十分困难——施加的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来解决这一问题，但在直接测量物体属性变化和可能发生的损坏方面，性能评估仍然是一个难题。为填补这一空白，本文引入了一种新概念，即利用仪器化物体来评估柔顺机器人抓取。文中提出了一种概念验证设计，用于从第一物体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式磁体的形变，能够检测其表面上的多个接触位置和施加的力。接触估计通过使用神经网络解释霍尔效应信号来实现。在综合实验中，该设计在估计长方体每个单面的接触方面取得了良好性能，并且在用于评估平行爪夹持器的抓取时，在检测来自多个面的接触方面表现出不错的性能，证明了该设计的有效性以及该概念的可行性。
英文摘要：
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.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
