基于FingerVision的触觉技能库的构建
Building a Library of Tactile Skills Based on FingerVision
摘要：
基于摄像头的触觉传感器正在成为一种有前途的廉价解决方案，用于增强的操纵任务。最近推出的FingerVision传感器能够产生可靠的信号，用于力估计，物体姿态估计和滑动检测。在本文中，我们建立在FingerVision设计的基础上，改进了现有的控制算法，更重要的是，通过利用原始皮肤变形数据进行控制，将其适用范围扩展到更具挑战性的任务。与依赖于整个传感器表面的平均变形的先前方法相比，我们直接采用浸入传感器的硅树脂主体中的每个球形标记的局部偏差用于反馈控制并作为学习任务的输入。我们表明，与这样的输入，不同的质地和粘度的物质可以区分的基础上引起的触觉，同时搅拌它们。作为另一个应用，我们学习皮肤变形和施加到对象的力之间的映射。为了展示所提出的控制器的全部功能，我们将它们部署在一个具有挑战性的建筑装配任务中，该任务涉及在最大负载点处的可弯曲板下方插入承重元件。
英文摘要：
Camera-based tactile sensors are emerging as a promising inexpensive solution for tactile-enhanced manipulation tasks. A recently introduced FingerVision sensor was shown capable of generating reliable signals for force estimation, object pose estimation, and slip detection. In this paper, we build upon the FingerVision design, improving already existing control algorithms, and, more importantly, expanding its range of applicability to more challenging tasks by utilizing raw skin deformation data for control. In contrast to previous approaches that rely on the average deformation of the whole sensor surface, we directly employ local deviations of each spherical marker immersed in the silicone body of the sensor for feedback control and as input to learning tasks. We show that with such input, substances of varying texture and viscosity can be distinguished on the basis of tactile sensations evoked while stirring them. As another application, we learn a mapping between skin deformation and force applied to an object. To demonstrate the full range of capabilities of the proposed controllers, we deploy them in a challenging architectural assembly task that involves inserting a load-bearing element underneath a bendable plate at the point of maximum load.
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。
