论文标题
使用外部摄像头的软握力的力/扭矩传感
Force/Torque Sensing for Soft Grippers using an External Camera
论文作者
论文摘要
机器人的操作可以受益于腕部安装的力/扭矩（F/T）传感器，但是传统的F/T传感器可能很昂贵，很难安装，并且高负载损坏。我们提出视觉力/扭矩传感（VFTS），该方法在视觉上估计了常规F/T传感器将报告的6轴F/T测量。与使用柔软的外表面后面的内部摄像头感觉负载的方法相反，我们的方法将外部摄像机与捕虫镜头一起观察到软抓手。 VFT包括一个深度学习模型，该模型将单个RGB图像作为输入，并输出6轴F/T估计值。我们使用传感器数据收集的传感器数据（从Hello Robot Inc.的Stretch Re1）培训了该模型，以执行操纵任务。 VFT的表现优于基于电动电流的F/T估计值，该估计值概括为新的家庭环境，并支持与医疗保健相关的三个自主任务：抓住毯子，将毯子拉到Manikin上并清洁Manikin的四肢。 VFT在手动操作的气动抓手方面也表现出色。总体而言，我们的结果表明，观察软抓手的外部摄像头可以为各种操纵任务执行有用的视觉力/扭矩感测。
Robotic manipulation can benefit from wrist-mounted force/torque (F/T) sensors, but conventional F/T sensors can be expensive, difficult to install, and damaged by high loads. We present Visual Force/Torque Sensing (VFTS), a method that visually estimates the 6-axis F/T measurement that would be reported by a conventional F/T sensor. In contrast to approaches that sense loads using internal cameras placed behind soft exterior surfaces, our approach uses an external camera with a fisheye lens that observes a soft gripper. VFTS includes a deep learning model that takes a single RGB image as input and outputs a 6-axis F/T estimate. We trained the model with sensor data collected while teleoperating a robot (Stretch RE1 from Hello Robot Inc.) to perform manipulation tasks. VFTS outperformed F/T estimates based on motor currents, generalized to a novel home environment, and supported three autonomous tasks relevant to healthcare: grasping a blanket, pulling a blanket over a manikin, and cleaning a manikin's limbs. VFTS also performed well with a manually operated pneumatic gripper. Overall, our results suggest that an external camera observing a soft gripper can perform useful visual force/torque sensing for a variety of manipulation tasks.
