3D assembly tasks, such as furniture assembly and component fitting, play a crucial role in daily life and represent essential capabilities for future home robots. Existing benchmarks and datasets predominantly focus on assembling geometric fragments or factory parts, which fall short in addressing the complexities of everyday object interactions and assemblies. To bridge this gap, we present 2BY2, a large-scale annotated dataset for daily pairwise objects assembly, covering 18 fine-grained tasks that reflect real-life scenarios, such as plugging into sockets, arranging flowers in vases, and inserting bread into toasters. 2BY2 dataset includes 1,034 instances and 517 pairwise objects with pose and symmetry annotations, requiring approaches that align geometric shapes while accounting for functional and spatial relationships between objects. Leveraging the 2BY2 dataset, we propose a two-step SE(3) pose estimation method with equivariant features for assembly constraints. Compared to previous shape assembly methods, our approach achieves state-of-the-art performance across all 18 tasks in the 2BY2 dataset. Additionally, robot experiments further validate the reliability and generalization ability of our method for complex 3D assembly tasks. More details and demonstrations can be found at https://tea-lab.github.io/TwoByTwo/.

In today's network-centric battlefield, the ad hoc style, self-organizing networks play a more and more important role in the operation of mobile forces that are deployed quickly to meet the tactical demands. However, the unique features of the tactical scenario also pose significant challenges for networking, which makes the existing research results for the general mobile ad hoc networks (MANETs) difficult to be reused directly. In this paper, we focus on the connectivity issue of the mobile tactical networks (MTNs). To better describe the special formation-oriented mobility pattern of MTNs, a more realistic node-following mobility model is used, based on which the neighbor connectivity is obtained from both the geometrical and physical communication point of view for not only 1-D but also 2-D network scenarios. After that, we further evolve our recently proposed decomposition and recursion method to derive the end-to-end connectivity for some formations commonly used in the MTNs. All our analytical results are verified by extensive simulations. We believe that this work is able to shed new lights on the MTNs' performance characteristics, which can be used for guiding the development of the next-generation MTNs.

Information-Centric Networking (ICN) has a great potential to better support the content distribution over the future Internet. Meanwhile, users with mobile devices will also access ICN, introducing a new challenge to ICN providers to handle such mobile content requests. In this paper, a mobility and popularity-based caching strategy is proposed to increase the cache hit rate through WiFi while people are moving from one WiFi hotspot to another. It thus lowers the network traffic from the local ICN to the outside Internet. Specifically, a precise derivation on the content request rate is provided based on the content popularity and user mobility. A novel caching strategy is further developed based on this derivation for both the mobility-oblivious and mobility-aware scenarios. We evaluate different caching strategies with a trace-driven simulation and our new approach can achieve a 33.84% reduction on the network traffic when compared with the caching strategies only considering the content popularity.