DeepFleet: Multi-Agent Foundation Models for Mobile Robots
Fuente:
arXiv
Guardado en:
| Autores principales: | , , , , , , , , , , , , , , , , , , , , |
|---|---|
| Formato: | Preprint |
| Publicado: |
2025
|
| Materias: | |
| Acceso en línea: | |
| Etiquetas: |
Agregar Etiqueta
Sin Etiquetas, Sea el primero en etiquetar este registro!
|
| _version_ | 1866910123843125248 |
|---|---|
| author | Agaskar, Ameya Siva, Sriram Pickering, William O'Brien, Kyle Kekeh, Charles Barbosa, Alexandre Ormiga Galvao Li, Ang Sarker, Brianna Gallo Chua, Alicia Nemade, Mayur Thattai, Charun Di, Jiaming Iyengar, Isaac Dharoor, Ramya Kirouani, Dino Erskine, Jimmy Hegazy, Tamir Niekum, Scott Khan, Usman A. Pecora, Federico Durham, Joseph W. |
| author_facet | Agaskar, Ameya Siva, Sriram Pickering, William O'Brien, Kyle Kekeh, Charles Barbosa, Alexandre Ormiga Galvao Li, Ang Sarker, Brianna Gallo Chua, Alicia Nemade, Mayur Thattai, Charun Di, Jiaming Iyengar, Isaac Dharoor, Ramya Kirouani, Dino Erskine, Jimmy Hegazy, Tamir Niekum, Scott Khan, Usman A. Pecora, Federico Durham, Joseph W. |
| contents | 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. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2508_08574 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | DeepFleet: Multi-Agent Foundation Models for Mobile Robots Agaskar, Ameya Siva, Sriram Pickering, William O'Brien, Kyle Kekeh, Charles Barbosa, Alexandre Ormiga Galvao Li, Ang Sarker, Brianna Gallo Chua, Alicia Nemade, Mayur Thattai, Charun Di, Jiaming Iyengar, Isaac Dharoor, Ramya Kirouani, Dino Erskine, Jimmy Hegazy, Tamir Niekum, Scott Khan, Usman A. Pecora, Federico Durham, Joseph W. Robotics Multiagent Systems I.2.9; I.2.11 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. |
| title | DeepFleet: Multi-Agent Foundation Models for Mobile Robots |
| topic | Robotics Multiagent Systems I.2.9; I.2.11 |
| url | https://arxiv.org/abs/2508.08574 |