Factory: Fast Contact for Robotic Assembly-猫眼课题宝

DOI：

10.48550/arxiv.2205.03532

发表时间：

2022-05

期刊：

ArXiv

影响因子：

通讯作者：

中科院分区：

其他

文献类型：

作者：

Yashraj S. Narang;Kier Storey;Iretiayo Akinola;M. Macklin;Philipp Reist;Lukasz Wawrzyniak;Yunrong Guo;Ádám Moravánszky;Gavriel State;Michelle Lu;Ankur Handa;D. Fox

机器人装配是机器人技术最古老、最具挑战性的应用之一。在机器人的其他领域，如感知和抓取，仿真迅速加速了研究进展，特别是与现代深度学习相结合时。然而，准确，高效，鲁棒地模拟装配中接触丰富的相互作用的范围仍然是一个长期的挑战。在这项工作中，我们提出了工厂，一套物理模拟方法和机器人学习工具，这样的应用程序。我们可以实时或更快地模拟各种接触丰富的场景，包括同时模拟1000个螺母和螺栓交互。我们提供60美元精心设计的零件模型，3个机器人装配环境和7个机器人控制器，用于训练和测试虚拟机器人。最后，我们训练和评估用于螺母和螺栓装配的概念验证强化学习策略。我们的目标是让Factory打开大门，将仿真用于机器人装配，以及机器人技术中许多其他接触丰富的应用。请访问https://sites.google.com/nvidia.com/factory获取补充内容，包括视频。

Robotic assembly is one of the oldest and most challenging applications of robotics. In other areas of robotics, such as perception and grasping, simulation has rapidly accelerated research progress, particularly when combined with modern deep learning. However, accurately, efficiently, and robustly simulating the range of contact-rich interactions in assembly remains a longstanding challenge. In this work, we present Factory, a set of physics simulation methods and robot learning tools for such applications. We achieve real-time or faster simulation of a wide range of contact-rich scenes, including simultaneous simulation of 1000 nut-and-bolt interactions. We provide $60$ carefully-designed part models, 3 robotic assembly environments, and 7 robot controllers for training and testing virtual robots. Finally, we train and evaluate proof-of-concept reinforcement learning policies for nut-and-bolt assembly. We aim for Factory to open the doors to using simulation for robotic assembly, as well as many other contact-rich applications in robotics. Please see https://sites.google.com/nvidia.com/factory for supplementary content, including videos.

Factory: Fast Contact for Robotic Assembly

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