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:在自然系统中,往往具有集体智能,使得它们能够自组织并适应变化,但在大多数人工系统中这种等效性是缺少的。我们探讨了在移动机器人上使用协作二维推动操作中这种系统的可能性。虽然先前的研究在有限的环境中展示了该问题的潜在解决方案,但存在计算和学习困难。更重要的是,这些系统在面临环境变化时没有适应性。在本研究中,我们展示了将从可微软体物理模拟器得到的规划器精简为基于注意力的神经网络时,我们的多机器人推动操作系统比基线实现了更好的性能。此外,我们的系统还适用于在训练期间未出现的配置,并能够适应于在任务完成时应用外部气流和环境变化。附加视频可以在我们的项目网站上找到:\url{https://sites.google.com/view/ciom/home}。