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.
翻译:机器人组装是机器人最古老和最具挑战性的应用之一。 在机器人的其他领域,例如认知和捕捉,模拟迅速加速了研究进展,特别是当与现代深层学习相结合时。然而,精确、高效和有力地模拟在组装中接触丰富的互动,仍然是一项长期挑战。在这项工作中,我们介绍工厂,一套物理模拟方法和机器人学习工具,用于此类应用。我们实现了对各种接触丰富的场景进行实时或更快的模拟,包括同时模拟1000个坚果和布尔特相互作用。我们提供了60美元的精心设计的部件模型、3个机器人组装环境以及7个机器人控制器,用于培训和测试虚拟机器人。最后,我们培训和评价了用于坚果和布尔特组装的校准强化概念学习政策。我们的目标是工厂为机器人组装的模拟以及机器人中许多其他接触丰富的应用程序打开大门。请参见 https://sites.google.com/nvidia.com/factory,用于补充内容,包括视频。