Despite significant advances in touch and force transduction, tactile sensing is still far from ubiquitous in robotic manipulation. Existing methods for building touch sensors have proven difficult to integrate into robot fingers due to multiple challenges, including difficulty in covering multicurved surfaces, high wire count, or packaging constrains preventing their use in dexterous hands. In this paper, we present a multicurved robotic finger with accurate touch localization and normal force detection over complex, three-dimensional surfaces. The key to our approach is the novel use of overlapping signals from light emitters and receivers embedded in a transparent waveguide layer that covers the functional areas of the finger. By measuring light transport between every emitter and receiver, we show that we can obtain a very rich signal set that changes in response to deformation of the finger due to touch. We then show that purely data-driven deep learning methods are able to extract useful information from such data, such as contact location and applied normal force, without the need for analytical models. The final result is a fully integrated, sensorized robot finger, with a low wire count and using easily accessible manufacturing methods, designed for easy integration into dexterous manipulators.

中文翻译：

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通过重叠光信号实现数据驱动触摸感应的传感化多曲面机器人手指

尽管在触摸和力传递方面取得了重大进展，但触觉传感在机器人操作中仍然远未普及。由于存在多种挑战，包括难以覆盖多曲面、高线数或封装限制，无法在灵巧的手中使用，因此现有的用于构建触摸传感器的方法已被证明难以集成到机器人手指中。在本文中，我们提出了一种多曲面机器人手指，在复杂的三维表面上具有精确的触摸定位和法向力检测。我们方法的关键是新颖地使用来自光发射器和接收器的重叠信号，这些信号嵌入在覆盖手指功能区域的透明波导层中。通过测量每个发射器和接收器之间的光传输，我们展示了我们可以获得非常丰富的信号集，该信号集会随着手指因触摸而变形而发生变化。然后，我们表明纯数据驱动的深度学习方法能够从此类数据中提取有用的信息，例如接触位置和施加的法向力，而无需分析模型。最终的结果是一个完全集成的、有传感器的机器人手指，具有较少的线数并使用易于访问的制造方法，专为轻松集成到灵巧的机械手而设计。