The field of robotics is increasingly focused on developing sophisticated multi-robot systems that can collaborate to achieve complex tasks. Recent research has made significant progress in addressing the challenges associated with task and motion planning, state reconstruction, and control synthesis for such systems. A key direction in this area is the development of methods that can efficiently plan and control the motions of multiple robots to satisfy high-level specifications, such as those expressed in temporal logic. These methods have the potential to enable robots to perform a wide range of tasks, from collaborative transportation of objects to complex manipulation and assembly tasks. Noteworthy papers in this area include one that presents a hierarchical temporal logic task and motion planning approach for multi-robot systems, which leverages the efficiency of hierarchical temporal logic specifications for task-level planning and the optimization-based graph of convex sets method for motion-level planning. Another notable paper proposes a biconvex method for minimum-time motion planning through sequences of convex sets, which quickly produces an initial trajectory and iteratively refines it by solving two convex subproblems in alternation. Additionally, a paper on TeLoGraF, Temporal Logic Graph-encoded Flow, utilizes Graph Neural Networks (GNN) encoder and flow-matching to learn solutions for general STL specifications, outperforming other baselines in the STL satisfaction rate and demonstrating 10-100X faster inference compared to classical STL planning algorithms.