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

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却缺乏这种对应能力。我们探讨了在移动机器人协同二维推拽操作中实现此类系统的可能性。尽管现有研究在受限环境下展示了该问题的潜在解决方案，但它们面临计算与学习困难。更重要的是，这些系统在面临环境变化时缺乏适应能力。本研究中，我们证明了通过将基于可微软体物理仿真器推导出的规划器蒸馏为基于注意力的神经网络，我们的多机器人推拽操作系统能取得优于基准方法的性能。此外，我们的系统还能泛化至训练中未见的配置，并在面临外部扰动与环境变化时自适应地完成任务。补充视频可在项目网站查看：https://sites.google.com/view/ciom/home