Force Sensing and Force Control are essential to many industrial applications. Typically, a 6-axis Force/Torque (F/T) sensor is mounted between the robot's wrist and the end-effector in order to measure the forces and torques exerted by the environment onto the robot (the external wrench). Although a typical 6-axis F/T sensor can provide highly accurate measurements, it is expensive and vulnerable to drift and external impacts. Existing methods aiming at estimating the external wrench using only the robot's internal signals are limited in scope: for example, wrench estimation accuracy was mostly validated in free-space motions and simple contacts as opposed to tasks like assembly that require high-precision force control. Here we present a Neural Network based method and argue that by devoting particular attention to the training data structure, it is possible to accurately estimate the external wrench in a wide range of scenarios based solely on internal signals. As an illustration, we demonstrate a pin insertion experiment with 100-micron clearance and a hand-guiding experiment, both performed without external F/T sensors or joint torque sensors. Our result opens the possibility of equipping the existing 2.7 million industrial robots with Force Sensing and Force Control capabilities without any additional hardware.

翻译：力觉感知与力控制是众多工业应用的核心要素。通常，在机器人腕部与末端执行器之间安装六维力/力矩传感器，用于测量环境施加于机器人的外力与力矩（即外载荷）。尽管典型的六维力/力矩传感器能提供高精度测量，但其成本高昂且易受漂移和外部冲击影响。现有仅利用机器人内部信号估算外载荷的方法存在局限性：例如，载荷估算精度大多在自由空间运动及简单接触场景中得到验证，而非装配等需要高精度力控制的任务。本文提出一种基于神经网络的方法，并论证通过精心设计训练数据结构，仅依靠内部信号即可在广泛场景中精确估算外载荷。作为示例，我们展示了间隙为100微米的销钉插入实验和手动引导实验，两项实验均未使用外部力/力矩传感器或关节力矩传感器。这一成果为全球现有270万台工业机器人赋予力觉感知与力控制能力（无需新增任何硬件）提供了可能性。