论文标题
使用快速3D重建和掌握质量CNN的6-DOF GRASP计划
6-DoF Grasp Planning using Fast 3D Reconstruction and Grasp Quality CNN
论文作者
论文摘要
最近对家用机器人的消费者需求加速了机器人握把的性能。但是，感知管道的关键组成部分，深度相机，对于大多数消费者来说仍然是昂贵且无法接近的。此外，通过利用大型数据集和云机器人技术，以及将状态和行动空间限制为具有4个自由度（DOF）的自上而下的抓地力，GRASP计划最近有了显着改善。通过使用廉价设备（例如现成的RGB摄像机和最先进的算法）（例如Learn Stereo Machine（LSM \ cite {KAR2017LEALNING}）等廉价设备（例如现成的RGB摄像机）和最先进的算法，该机器人能够从与6-DOF不同的速度产生更多的健壮的抓物。在本文中，我们提出了LSM的修改，以掌握可抓握的对象，评估抓取物，并基于Grasp-Cnn \ cite {Mahler2017dex}）基于Grasp-Cnn \ cite {Mahler2017dex}）的6-DOF GRASP，从而利用了多个摄像头的观点，以计划强大的计划，即使在没有可能的盖帽上，也可能会有可能的抓地力。
Recent consumer demand for home robots has accelerated performance of robotic grasping. However, a key component of the perception pipeline, the depth camera, is still expensive and inaccessible to most consumers. In addition, grasp planning has significantly improved recently, by leveraging large datasets and cloud robotics, and by limiting the state and action space to top-down grasps with 4 degrees of freedom (DoF). By leveraging multi-view geometry of the object using inexpensive equipment such as off-the-shelf RGB cameras and state-of-the-art algorithms such as Learn Stereo Machine (LSM\cite{kar2017learning}), the robot is able to generate more robust grasps from different angles with 6-DoF. In this paper, we present a modification of LSM to graspable objects, evaluate the grasps, and develop a 6-DoF grasp planner based on Grasp-Quality CNN (GQ-CNN\cite{mahler2017dex}) that exploits multiple camera views to plan a robust grasp, even in the absence of a possible top-down grasp.
