In this paper we present an implementation of an interest management scheme using standard message oriented middleware (MOM) technologies to provide scalable message dissemination for networked games. The aim of all interest management schemes is to identify when objects that inhabit a virtual world should be interacting and to enable such interaction via message passing while preventing objects that should not be interacting from exchanging messages. The time taken by existing interest management schemes to resolve which objects influence each other may be too large to enable the desired interaction to occur. Furthermore, existing interest management implementations tend to be proprietary and are built directly on top of networking protocols. In this paper we present an approach to interest management based on the predicted movement of objects. Our approach determines the frequency of message exchange between objects on the likelihood that such objects will influence each other in the near future. We then demonstrate, via implementation and experimentation, how existing middleware standards provide a suitable platform for the deployment of our interest management scheme.
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 carefullydesigned 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 our website for supplementary content, including videos. 1
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Abstract. We present a collision detection algorithm (Expanding Spheres) for interest management in networked games. The aim of all interest management schemes is to identify when objects that inhabit a virtual world should be interacting and to enable such interaction via message passing while preventing objects that should not be interacting from exchanging messages. Preventing unnecessary message exchange provides a more scalable solution for networked games. A collision detection algorithm is required by interest management schemes as object interaction is commonly determined by object location in the virtual world: the closer objects are to each other the more likely they are to interact. The collision detection algorithm presented in this paper is designed specifically for interest management schemes and produces accurate results when determining object interactions. We present performance figures that indicate that our collision detection algorithm is scalable.
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 carefullydesigned 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 our website for supplementary content, including videos. 1
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