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: this https URL

中文翻译：

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使用多指触觉机器人手进行抓握稳定的滑动检测

人类在抓握时使用触觉来防止我们掉落物体。触觉感知的一个关键方面是滑动检测，它允许抓手知道何时抓取失败并采取措施防止物体掉落。本研究通过使用支持向量机来检测滑动并测试多种滑动场景，包括实时响应各种抓握中的 11 种不同物体的滑动开始，证明了最近开发的 Tactile Model O (T-MO) 的滑动检测能力。我们通过测试两个真实世界的场景来证明滑动检测在抓取中的好处：增加重量以破坏抓取的稳定性和使用滑动检测在第一次尝试时抬起物体。T-MO 能够检测物体何时滑动，做出反应以稳定抓握并部署在现实世界中。这表明 T-MO 是一个合适的自主抓取平台，通过使用可靠的滑动检测来确保在非结构化环境中的稳定抓取。补充视频：这个https URL