We focus on the problem of rearranging a set of objects with a team of car-like robot pushers built using off-the-shelf components. Maintaining control of pushed objects while avoiding collisions in a tight space demands highly coordinated motion that is challenging to execute on constrained hardware. Centralized replanning approaches become intractable even for small-sized problems whereas decentralized approaches often get stuck in deadlocks. Our key insight is that by carefully assigning pushing tasks to robots, we could reduce the complexity of the rearrangement task, enabling robust performance via scalable decentralized control. Based on this insight, we built PuSHR, a system that optimally assigns pushing tasks and trajectories to robots offline, and performs trajectory tracking via decentralized control online. Through an ablation study in simulation, we demonstrate that PuSHR dominates baselines ranging from purely decentralized to fully decentralized in terms of success rate and time efficiency across challenging tasks with up to 4 robots. Hardware experiments demonstrate the transfer of our system to the real world and highlight its robustness to model inaccuracies. Our code can be found at https://github.com/prl-mushr/pushr, and videos from our experiments at https://youtu.be/DIWmZerF_O8.

翻译：我们聚焦于利用基于现成组件构建的类车推式机器人团队重新排列一组物体的难题。在狭小空间中控制被推物体并避免碰撞，需要高度协调的运动，这在受限硬件上极具挑战性。集中式重规划方法即使在小规模问题中也变得难以处理，而分散式方法常陷入死锁。我们的关键洞察在于：通过将推取任务合理分配给各机器人，可降低重新排列任务的复杂度，从而借助可扩展的分散式控制实现鲁棒性能。基于这一洞见，我们构建了PuSHR系统，该系统能够离线为机器人最优分配推取任务与轨迹，并通过在线分散式控制执行轨迹跟踪。通过仿真中的消融实验，我们证明PuSHR在最多4台机器人的挑战性任务中，在成功率和时间效率上均优于从纯集中式到全分散式的基线方法。硬件实验验证了该系统向真实场景的迁移能力，并突出其对模型误差的鲁棒性。我们的代码详见https://github.com/prl-mushr/pushr，实验视频请见https://youtu.be/DIWmZerF_O8。