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: \url{https://sites.google.com/view/ciom/home}.

翻译：尽管自然系统常展现出使它们能够自组织并适应变化的集体智能，但大多数人工系统中缺乏类似特性。我们探索了在移动机器人协同二维推操作背景下实现此类系统的可能性。虽然现有工作在受限环境下展示了潜在解决方案，但这些方法存在计算与学习困难。更重要的是，这些系统在面临环境变化时缺乏适应能力。本研究表明，通过将基于可微软体物理模拟器推导的规划器蒸馏为注意力神经网络，我们的多机器人推操作系统实现了优于基准方法的性能。此外，该系统还能泛化至训练中未见过的配置，并在施加外部扰动与环境变化时保持任务完成的适应性。补充视频可在项目网站获取：\url{https://sites.google.com/view/ciom/home}。