NRI: FND: Scalable Multimodal Tactile Sensing for Robotic Manipulators in Manufacturing

这项研究的重点是传感器和软件，使机器人具有人工触觉。目前的机器人手和抓取器很少有感知环境的能力；它们通过精确地重复预先设定好的动作来运作。这阻止了机器人在预先不可知世界状态的应用中使用，因为这种使用需要机器人感知并对意外事件做出反应。该项目将设计新颖复杂的触摸传感器，并将其装备在机器人手上。传感器将包括多种类型的触摸传感器，并将它们集成在一起，以检测不同类型的接触和特征：快速与慢速，短暂与持续等。这些数据将由学习如何利用新获得的触摸数据的软件算法进行解释。这种方法的灵感来自人手，人手也配备了多种触觉传感元件，将信息传递给神经系统。这项工作的主要目标是为在混乱的环境中处理材料构建工具，这项任务可以使电子商务和供应链更有效地运行，帮助制造商提高效率和竞争力，并通过协助已知会造成伤害的重复性任务来减少对工人的伤害。新颖的传感器设计、计算和规划将被整合在一起，赋予机器人触觉，从而使其能够在混乱的环境中进行操作。在硬件层面，传感器将结合压电和电阻应变传感来捕获瞬态、高频响应和绝对应变测量。传感器片将堆叠在多个传感层中，以提供丰富的信号集，而不是通常采用的“一个位置，一个taxel”方法，并通过结合基于模型和数据驱动的方法来最大限度地利用丰富的数据来定义电机控制原语。这些将结合起来实现运动控制和规划的算法，在目标任务中使用触觉数据：在制造中拾取垃圾箱和装配。总体目标是为机器人操纵者提供一个紧凑，多模态，可扩展的触觉传感系统，以及低级运动技能和高级规划算法，这些算法使用触觉数据在混乱中进行操作。

The research focuses on sensors and software to enable robots to have an artificial sense of touch. Current robot hands and grippers rarely have the ability to sense their environments; they operate by precisely repeating pre-programmed motions. This prevents the use of robots in applications where the state of the world is unknowable in advance, since such use requires a robot to sense and react to unexpected events. The project will design novel and sophisticated touch sensors and equip robot hands with them. The sensors will include multiple types of touch sensors and integrate them to detect different types and characteristics of contacts: fast versus slow, transitory versus maintained, etc. These data will be interpreted by software algorithms that learn how to make use of the newly acquired touch data. This approach takes its inspiration from the human hand, which is also equipped with multiple types of tactile sensing elements relaying information to the nervous system. The proximate goal of the work is to build tools for material handling in cluttered environments, a task that can enable e-commerce and supply chains to run more efficiently, help manufacturers become more efficient and competitive, and reduce injuries to workers by assisting with repetitive tasks that are known to cause injuries.Novel sensor design, computation and planning will be integrated to endow robots with a sense of touch, thereby enabling manipulation in cluttered environments. At the hardware level, the sensors will combine piezoelectric and resistive strain sensing to capture both transient, high-frequency responses and absolute strain measurements. Sensor sheets will be stacked in multiple sensing layers to provide a rich signal set that departs from the "one location, one taxel" approach that is normally taken and maximum use will be made of the rich data by combining model-based and data-driven approaches to define motor control primitives. These will be combined to implement algorithms for motor control and planning that use the tactile data in a target task: bin-picking and kitting in manufacturing. The overall goal is a compact, multi-modal, scalable tactile sensing system for robotic manipulators, along with the low-level motor skills and high level planning algorithms that use tactile data for manipulating in clutter.