论文标题
Digitac：用于比较低成本高分辨率机器人触摸的Digit-Tactip混合动力传感器
DigiTac: A DIGIT-TacTip Hybrid Tactile Sensor for Comparing Low-Cost High-Resolution Robot Touch
论文作者
论文摘要
深度学习与高分辨率的触觉感应相结合可能导致高功能强大的灵巧机器人。但是，由于专业设备和专业知识，进度很慢。数字触觉传感器使用Gelsight型传感器提供了低分辨率触摸的低成本入口。在这里，我们根据柔软的仿生光学触觉传感器的Tactip家族定制数字以具有3D打印的传感表面。 Digit-Tactip（Digitac）可以在这些不同的触觉传感器类型之间进行直接比较。为了进行此比较，我们引入了一个触觉机器人系统，该机器人系统包括桌面臂，安装座和3D打印的测试对象。我们将触觉伺服器控制与Posenet深度学习模型一起比较数字，Digitac和tactip，以在3D形状上进行边缘和表面跟随。这三个传感器在姿势预测上的性能类似，但是它们的构造导致了伺服控制的不同性能，为研究人员选择或创新触觉传感器提供了指导。复制该研究的所有硬件和软件将公开发布。项目网站：www.lepora.com/digitac。项目存储库：www.github.com/nlepora/digitac-design。
Deep learning combined with high-resolution tactile sensing could lead to highly capable dexterous robots. However, progress is slow because of the specialist equipment and expertise. The DIGIT tactile sensor offers low-cost entry to high-resolution touch using GelSight-type sensors. Here we customize the DIGIT to have a 3D-printed sensing surface based on the TacTip family of soft biomimetic optical tactile sensors. The DIGIT-TacTip (DigiTac) enables direct comparison between these distinct tactile sensor types. For this comparison, we introduce a tactile robot system comprising a desktop arm, mounts and 3D-printed test objects. We use tactile servo control with a PoseNet deep learning model to compare the DIGIT, DigiTac and TacTip for edge- and surface-following over 3D-shapes. All three sensors performed similarly at pose prediction, but their constructions led to differing performances at servo control, offering guidance for researchers selecting or innovating tactile sensors. All hardware and software for reproducing this study will be openly released. Project website: www.lepora.com/digitac. Project repository: www.github.com/nlepora/digitac-design.
