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

Collective Intelligence for 2D Push Manipulations with Mobile Robots