  While natural systems often present collective intelligence that allows them
to self-organize and adapt to changes, the equivalent is missing in most
artificial systems. We explore the possibility of such a system in the context
of cooperative 2D push manipulations using mobile robots. Although conventional
works demonstrate potential solutions for the problem in restricted settings,
they have computational and learning difficulties. More importantly, these
systems do not possess the ability to adapt when facing environmental changes.
In this work, we show that by distilling a planner derived from a
differentiable soft-body physics simulator into an attention-based neural
network, our multi-robot push manipulation system achieves better performance
than baselines. In addition, our system also generalizes to configurations not
seen during training and is able to adapt toward task completions when external
turbulence and environmental changes are applied. Supplementary videos can be
found on our project website: https://sites.google.com/view/ciom/home
