In this paper, we present an approach for integrated task and motion planning based on an AND/OR graph network, which is used to represent task-level states and actions, and we leverage it to implement different classes of task and motion planning problems (TAMP). Several problems that fall under task and motion planning do not have a predetermined number of sub-tasks to achieve a goal. For example, while retrieving a target object from a cluttered workspace, in principle the number of object re-arrangements required to finally grasp it cannot be known ahead of time. To address this challenge, and in contrast to traditional planners, also those based on AND/OR graphs, we grow the AND/OR graph at run-time by progressively adding sub-graphs until grasping the target object becomes feasible, which yields a network of AND/OR graphs. The approach is extended to enable multi-robot task and motion planning, and (i) it allows us to perform task allocation while coordinating the activity of a given number of robots, and (ii) can handle multi-robot tasks involving an a priori unknown number of sub-tasks. The approach is evaluated and validated both in simulation and with a real dual-arm robot manipulator, that is, Baxter from Rethink Robotics. In particular, for the single-robot task and motion planning, we validated our approach in three different TAMP domains. Furthermore, we also use three different robots for simulation, namely, Baxter, Franka Emika Panda manipulators, and a PR2 robot. Experiments show that our approach can be readily scaled to scenarios with many objects and robots, and is capable of handling different classes of TAMP problems.

本研究针对集成任务与运动规划（TAMP）中的一个关键问题，即在执行目标时无法预知子任务数量的挑战，提出了一种基于AND/OR图网络的方法。该方法通过在运行时动态扩展图，将任务分配与多机器人协调结合起来，从而有效处理未知子任务数量的多机器人任务。实验结果表明，该方法能够扩展到多个对象和机器人的场景，并适应不同类型的TAMP问题。

基于迭代加深AND/OR图网络的任务与运动规划框架