We introduce DeepFleet, a suite of foundation models designed to support coordination and planning for large-scale mobile robot fleets. These models are trained on fleet movement data, including robot positions, goals, and interactions, from hundreds of thousands of robots in Amazon warehouses worldwide. DeepFleet consists of four architectures that each embody a distinct inductive bias and collectively explore key points in the design space for multi-agent foundation models: the robot-centric (RC) model is an autoregressive decision transformer operating on neighborhoods of individual robots; the robot-floor (RF) model uses a transformer with cross-attention between robots and the warehouse floor; the image-floor (IF) model applies convolutional encoding to a multi-channel image representation of the full fleet; and the graph-floor (GF) model combines temporal attention with graph neural networks for spatial relationships. In this paper, we describe these models and present our evaluation of the impact of these design choices on prediction task performance. We find that the robot-centric and graph-floor models, which both use asynchronous robot state updates and incorporate the localized structure of robot interactions, show the most promise. We also present experiments that show that these two models can make effective use of larger warehouses operation datasets as the models are scaled up.

翻译：[translated abstract in Chinese] 我们提出DeepFleet，一系列旨在支持大规模移动机器人集群协调与规划的基础模型。这些模型利用全球亚马逊仓库中数十万个机器人的集群运动数据（包括位置、目标及交互）进行训练。DeepFleet包含四种架构，每种都体现了特定的归纳偏置，并共同探索了多智能体基础模型设计空间中的关键点：机器人中心模型（RC）是一种基于个体机器人邻域的自回归决策变换器；机器人-地面模型（RF）采用在机器人与仓库地面之间具有交叉注意力的变换器；图像-地面模型（IF）将整个集群的多通道图像表示进行卷积编码；而图-地面模型（GF）则将时序注意力与图神经网络相结合以处理空间关系。本文描述了这些模型，并评估了设计选择对预测任务性能的影响。研究发现，采用异步机器人状态更新且融入局部化机器人交互结构的机器人中心模型和图-地面模型最具潜力。我们还通过实验表明，随着模型规模的扩展，这两种模型能有效利用更大规模的仓库运行数据集。