A central research topic in robotics is how to use this system to interact with the physical world. Traditional manipulation tasks primarily focus on small objects. However, in factory or home environments, there is often a need for the movement of large objects, such as moving tables. These tasks typically require multi-robot systems to work collaboratively. Previous research lacks a framework that can scale to arbitrary sizes of robots and generalize to various kinds of tasks. In this work, we propose CollaBot, a generalist framework for simultaneous collaborative manipulation. First, we use SEEM for scene segmentation and point cloud extraction of the target object. Then, we propose a collaborative grasping framework, which decomposes the task into local grasp pose generation and global collaboration. Finally, we design a 2-stage planning module that can generate collision-free trajectories to achieve this task. Experiments show a success rate of 52% across different numbers of robots, objects, and tasks, indicating the effectiveness of the proposed framework.

-- Indoor localization using deep learning (DL) has demonstrated strong accuracy in mapping Wi - Fi RSS fingerprints to physical locations; however, most existing DL frameworks function as black - box models, offering limited insight into how predictions are made or how models respond to real - world noise over time. This lack of interpretability hampers our ability to understand the impact of temporal variations -- caused by environmental dynamics -- and to adapt models for long - term reliability. To address thi s, we introduce LogNet, a novel logic gate - based framework designed to interpret and enhance DL - based indoor localization. LogNet enables transparent reasoning by identifying which access points (APs) are most influential for each reference point (RP) and reveals how environmental noise disrupts DL - driven localization decisions . This interpretability allows us to trace and diagnose model failures and adapt DL systems for more stable long - term deployment s . Evaluations across multiple real - world building floo rplans and over two years of temporal variation show that LogNet not only interprets the internal behavior of DL models but also improves performance -- achieving up to 1. 1 to 2 . Indoor localization has become a cornerstone of modern context - aware technologies, enabling applications in robotics, augmented and virtual reality (AR/VR), asset tracking, and emergency response. One of the earliest indoor localization system, " The Active Badge Location System " introduced in 1992 [1], relied on infrared (IR) pulses emitted by wearable badges and captured by stationary IR receivers [ 1 ].