论文标题
使用RF感知对完全封闭的物体的机器人抓握
Robotic Grasping of Fully-Occluded Objects using RF Perception
论文作者
论文摘要
我们介绍了RF-Grasp的设计，实现和评估，RF-Grasp是一种机器人系统，可以在未知和非结构化环境中掌握完全封闭的对象。与先前受视觉和红外传感器视线感知的限制的系统不同，RF-GRASP采用RF（射频）感知来通过遮挡来识别和定位目标对象，并在非线视觉设置中执行有效的探索和复杂的操纵任务。 RF-grasp依赖于目光相机和连接到感兴趣对象的无电池RFID标签。它介绍了两个主要创新：（1）RF-Visual伺服控制器，该控制器使用RFID的位置选择性地探索环境并计划有效的轨迹，以及（2）RF-Visal-Visual Deep ForcorceNtion网络，可以学习和执行高效，复杂的策略，以拆毁和彻底拆卸和求助。 我们实施并评估了RF-Grasp的端到端物理原型。我们证明，它在最先进的基准中提高了成功率和效率高达40-50％。我们还展示了RF-Grasp在新颖的任务中，这种机械搜索障碍物背后的完全封闭的物体，为机器人操纵打开了新的可能性。 rfgrasp.media.mit.edu可用的定性结果（视频）
We present the design, implementation, and evaluation of RF-Grasp, a robotic system that can grasp fully-occluded objects in unknown and unstructured environments. Unlike prior systems that are constrained by the line-of-sight perception of vision and infrared sensors, RF-Grasp employs RF (Radio Frequency) perception to identify and locate target objects through occlusions, and perform efficient exploration and complex manipulation tasks in non-line-of-sight settings. RF-Grasp relies on an eye-in-hand camera and batteryless RFID tags attached to objects of interest. It introduces two main innovations: (1) an RF-visual servoing controller that uses the RFID's location to selectively explore the environment and plan an efficient trajectory toward an occluded target, and (2) an RF-visual deep reinforcement learning network that can learn and execute efficient, complex policies for decluttering and grasping. We implemented and evaluated an end-to-end physical prototype of RF-Grasp. We demonstrate it improves success rate and efficiency by up to 40-50% over a state-of-the-art baseline. We also demonstrate RF-Grasp in novel tasks such mechanical search of fully-occluded objects behind obstacles, opening up new possibilities for robotic manipulation. Qualitative results (videos) available at rfgrasp.media.mit.edu
