Directional Semantic Grasping of Real-World Objects: From Simulation to Reality

Directional Semantic Grasping of Real-World Objects: From Simulation to Reality
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现实世界对象的定向语义抓取:从模拟到现实

DOI:
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发表时间:
2019
期刊:
arXiv.org
影响因子:
--
通讯作者:
Stan Birchfield
Stan Birchfield
中科院分区:
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文献类型:
--
作者:
Shariq Iqbal;Jonathan Tremblay;Thang To;Jia Cheng;Erik Leitch;Andy Campbell;Kirby Leung;Duncan McKay;Stan Birchfield

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我们提出了一种深度强化学习方法,从特定方向把握语义有意义的对象。该系统完全在模拟中训练,通过使用模拟物理接触并产生具有域随机背景的逼真图像的模拟器完成模拟到真实的转移。该系统是从多个预定义的以对象为中心的取向(诸如从侧面或顶部)端到端(将输入单目RGB图像映射到输出笛卡尔运动命令)抓取对象的示例。再加上一个实时的6自由度物体姿态估计器,手眼系统能够在可抓取的工作空间内的任何地方抓取物体。仿真和真实的世界的结果表明,该方法的有效性。
We present a deep reinforcement learning approach to grasp semantically meaningful objects from a particular direction. The system is trained entirely in simulation, with sim-to-real transfer accomplished by using a simulator that models physical contact and produces photorealistic imagery with domain randomized backgrounds. The system is an example of end-to-end (mapping input monocular RGB images to output Cartesian motor commands) grasping of objects from multiple pre-defined object-centric orientations, such as from the side or top. Coupled with a real-time 6-DoF object pose estimator, the eye-in-hand system is capable of grasping objects anywhere within the graspable workspace. Results are shown in both simulation and the real world, demonstrating the effectiveness of the approach.
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