论文标题
用多指的触觉机器人手掌握稳定的滑动检测
Slip detection for grasp stabilisation with a multi-fingered tactile robot hand
论文作者
论文摘要
抓住时，人类使用触觉感应，以防止我们掉落物体。触觉传感的一个关键方面是滑动检测，它使抓手知道何时掌握失败并采取行动以防止对象掉落。这项研究证明了最近开发的触觉模型O（T-MO）的滑动检测能力，它通过使用支撑矢量机来检测滑移和测试多种滑移场景，包括在各种掌管中使用11个不同的物体实时响应滑移的发作。我们通过测试两种现实世界的情况来证明抓住滑动检测的好处：增加重量以破坏掌握的稳定并使用滑移检测在第一次尝试时提起对象。 T-MO能够检测物体何时滑动，反应以稳定掌握并在现实世界中部署。这表明T-MO是通过使用可靠的滑移检测来确保在非结构化环境中稳定掌握的合适平台。补充视频：https：//youtu.be/wowfhaihuky
Tactile sensing is used by humans when grasping to prevent us dropping objects. One key facet of tactile sensing is slip detection, which allows a gripper to know when a grasp is failing and take action to prevent an object being dropped. This study demonstrates the slip detection capabilities of the recently developed Tactile Model O (T-MO) by using support vector machines to detect slip and test multiple slip scenarios including responding to the onset of slip in real time with eleven different objects in various grasps. We demonstrate the benefits of slip detection in grasping by testing two real-world scenarios: adding weight to destabilise a grasp and using slip detection to lift up objects at the first attempt. The T-MO is able to detect when an object is slipping, react to stabilise the grasp and be deployed in real-world scenarios. This shows the T-MO is a suitable platform for autonomous grasping by using reliable slip detection to ensure a stable grasp in unstructured environments. Supplementary video: https://youtu.be/wOwFHaiHuKY
