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 object manipulation 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 gradient-based soft-body physics simulator into an attention-based neural network, our multi-robot manipulation system can achieve better performance than baselines. In addition, our system also generalizes to unseen configurations during training and is able to adapt toward task completions when external turbulence and environmental changes are applied.

翻译：虽然自然系统往往提供集体智慧,允许它们自我组织和适应变化,但在大多数人工系统中却缺少等效的系统。我们在使用移动机器人的合作性物体操纵的背景下探索了这种系统的可能性。虽然常规工程显示了在限制环境下解决问题的潜在解决方案,但它们在计算和学习方面有困难。更重要的是,这些系统在面对环境变化时不具备适应能力。在这项工作中,我们通过从基于梯度的软体物理学模拟器中提取一个规划器进入一个以注意为基础的神经网络,我们多机器人操作系统能够取得比基线更好的性能。此外,我们的系统在培训过程中还概括了看不见的配置,能够在应用外部动荡和环境变化时适应任务完成。