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.

通过将基于梯度的软体物理模拟器的规划器提炼成基于注意力机制的神经网络，我们的多机器人操作系统在适应环境变化、处理任务时表现更好，并且可以应用于训练中未见过的配置。