Many robots are not equipped with a manipulator and many objects are not suitable for prehensile manipulation (such as large boxes and cylinders). In these cases, pushing is a simple yet effective non-prehensile skill for robots to interact with and further change the environment. Existing work often assumes a set of predefined pushing modes and fixed-shape objects. This work tackles the general problem of controlling a robotic fleet to push collaboratively numerous arbitrary objects to respective destinations, within complex environments of cluttered and movable obstacles. It incorporates several characteristic challenges for multi-robot systems such as online task coordination under large uncertainties of cost and duration, and for contact-rich tasks such as hybrid switching among different contact modes, and under-actuation due to constrained contact forces. The proposed method is based on combinatorial hybrid optimization over dynamic task assignments and hybrid execution via sequences of pushing modes and associated forces. It consists of three main components: (I) the decomposition, ordering and rolling assignment of pushing subtasks to robot subgroups; (II) the keyframe guided hybrid search to optimize the sequence of parameterized pushing modes for each subtask; (III) the hybrid control to execute these modes and transit among them. Last but not least, a diffusion-based accelerator is adopted to predict the keyframes and pushing modes that should be prioritized during hybrid search; and further improve planning efficiency. The framework is complete under mild assumptions. Its efficiency and effectiveness under different numbers of robots and general-shaped objects are validated extensively in simulations and hardware experiments, as well as generalizations to heterogeneous robots, planar assembly and 6D pushing.

翻译：许多机器人未配备机械臂，且大量物体（如大型箱体与圆柱体）不适合抓取式操作。在此类场景下，推动作为一种简单有效的非抓取式技能，可使机器人与环境交互并进一步改变环境状态。现有研究通常预设固定的推动模式与规则形状物体。本文针对复杂杂乱且含可移动障碍物的环境，研究控制机器人集群协作推动任意形状物体至各自目标位置这一通用问题。该方法需应对多机器人系统的典型挑战（如成本与耗时存在高度不确定性时的在线任务协调），以及接触密集型任务的特有难点（如不同接触模式间的混合切换、接触力受限导致的欠驱动特性）。所提方法基于动态任务分配的组合混合优化，通过推动模式序列及相关作用力实现混合执行，包含三个核心组件：（I）将推动子任务分解、排序并滚动分配给机器人子群；（II）通过关键帧引导的混合搜索优化每个子任务的参数化推动模式序列；（III）执行这些模式并在其间切换的混合控制。此外，采用基于扩散模型的加速器预测混合搜索中应优先处理的关键帧与推动模式，从而提升规划效率。该框架在温和假设下具备完备性，通过大量仿真与硬件实验验证了其在不同机器人数量与任意形状物体下的效率与有效性，并可泛化至异构机器人、平面装配与六维推动任务。