# DeepFleet: Multi-Agent Foundation Models for Mobile Robots arXiv: https://arxiv.org/abs/2508.08574 · HTML: https://mlsanity.com/paper/2508.08574/ ## Key facts - Cited by 4 papers in the map (data through 14 Oct 2026). - Placed in Multimodal and embodied agents, one of 16 directions in LLM agents and tool use. - Builds on 47 of its 70 references that are in the map. - First posted on arXiv 12 Aug 2025. ## Placed in - LLM agents and tool use / [Multimodal and embodied agents](https://mlsanity.com/agents/multimodal-and-embodied-agents/) ## Builds on (most cited first, 30 of 47) - [Learning Transferable Visual Models From Natural Language Supervision](https://mlsanity.com/paper/2103.00020/) (2021) - [Language Models are Few-Shot Learners](https://mlsanity.com/paper/2005.14165/) (2020) - [The Llama 3 Herd of Models](https://mlsanity.com/paper/2407.21783/) (2024) - [Scaling Laws for Neural Language Models](https://mlsanity.com/paper/2001.08361/) (2020) - [PaLM: Scaling Language Modeling with Pathways](https://mlsanity.com/paper/2204.02311/) (2022) - [Mamba: Linear-Time Sequence Modeling with Selective State Spaces](https://mlsanity.com/paper/2312.00752/) (2023) - [Flamingo: a Visual Language Model for Few-Shot Learning](https://mlsanity.com/paper/2204.14198/) (2022) - [RT-2: Vision-Language-Action Models Transfer Web Knowledge to Robotic Control](https://mlsanity.com/paper/2307.15818/) (2023) - [Training Compute-Optimal Large Language Models](https://mlsanity.com/paper/2203.15556/) (2022) - [π0: A Vision-Language-Action Flow Model for General Robot Control](https://mlsanity.com/paper/2410.24164/) (2024) - [RT-1: Robotics Transformer for Real-World Control at Scale](https://mlsanity.com/paper/2212.06817/) (2022) - [Decision Transformer: Reinforcement Learning via Sequence Modeling](https://mlsanity.com/paper/2106.01345/) (2021) - [Octo: An Open-Source Generalist Robot Policy](https://mlsanity.com/paper/2405.12213/) (2024) - [Self-Attention with Relative Position Representations](https://mlsanity.com/paper/1803.02155/) (2018) - [Perceiver: General Perception with Iterative Attention](https://mlsanity.com/paper/2103.03206/) (2021) - [Train Short, Test Long: Attention with Linear Biases Enables Input Length Extrapolation](https://mlsanity.com/paper/2108.12409/) (2021) - [A Generalist Agent](https://mlsanity.com/paper/2205.06175/) (2022) - [Social GAN: Socially Acceptable Trajectories with Generative Adversarial Networks](https://mlsanity.com/paper/1803.10892/) (2018) - [Perceiver-Actor: A Multi-Task Transformer for Robotic Manipulation](https://mlsanity.com/paper/2209.05451/) (2022) - [VIMA: General Robot Manipulation with Multimodal Prompts](https://mlsanity.com/paper/2210.03094/) (2022) - [A Survey on Video Diffusion Models](https://mlsanity.com/paper/2310.10647/) (2023) - [Multi-Agent Pathfinding: Definitions, Variants, and Benchmarks](https://mlsanity.com/paper/1906.08291/) (2019) - [Transformer Networks for Trajectory Forecasting](https://mlsanity.com/paper/2003.08111/) (2020) - [GenCast: Diffusion-based ensemble forecasting for medium-range weather](https://mlsanity.com/paper/2312.15796/) (2023) - [From Goals, Waypoints & Paths To Long Term Human Trajectory Forecasting](https://mlsanity.com/paper/2012.01526/) (2020) - [PRIMAL: Pathfinding via Reinforcement and Imitation Multi-Agent Learning](https://mlsanity.com/paper/1809.03531/) (2018) - [Searching with Consistent Prioritization for Multi-Agent Path Finding](https://mlsanity.com/paper/1812.06356/) (2018) - [EECBS: A Bounded-Suboptimal Search for Multi-Agent Path Finding](https://mlsanity.com/paper/2010.01367/) (2020) - [PRIMAL2: Pathfinding via Reinforcement and Imitation Multi-Agent Learning -- Lifelong](https://mlsanity.com/paper/2010.08184/) (2020) - [Priority Inheritance with Backtracking for Iterative Multi-agent Path Finding](https://mlsanity.com/paper/1901.11282/) (2019) ## Built on by (most cited first, 4 of 4) - [Pairwise is Not Enough: Hypergraph Neural Networks for Multi-Agent Pathfinding](https://mlsanity.com/paper/2602.06733/) (2026) - [DF-ExpEnse: Diffusion Filtered Exploration for Sample Efficient Finetuning](https://mlsanity.com/paper/2606.19656/) (2026) - [Multi-robot Path Planning and Scheduling via Model Predictive Optimal Transport (MPC-OT)](https://mlsanity.com/paper/2508.21205/) (2025) - [Position: Beyond Training Time, Test-Time Coordination is Essential for Cooperative MARL](https://mlsanity.com/paper/2610.17409/) (2026) ## Abstract (arXiv) 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.