  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.
