# Robots as Tokens: Unified Diffusion Transformer for Coordinated Multi-Robot Trajectory Generation

arXiv: https://arxiv.org/abs/2606.15550 · HTML: https://mlsanity.com/paper/2606.15550/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Placed in Diffusion and flow policies for robotics, one of 14 directions in Diffusion models.
- Builds on 26 of its 31 references that are in the map.
- First posted on arXiv 14 Jun 2026.

## Placed in

- Diffusion models / [Diffusion and flow policies for robotics](https://mlsanity.com/diffusion/diffusion-and-flow-policies-for-robotics/)

## Builds on (most cited first, 26 of 26)

- [Attention Is All You Need](https://mlsanity.com/paper/1706.03762/) (2017)
- [Deep Residual Learning for Image Recognition](https://mlsanity.com/paper/1512.03385/) (2015)
- [Denoising Diffusion Probabilistic Models](https://mlsanity.com/paper/2006.11239/) (2020)
- [High-Resolution Image Synthesis with Latent Diffusion Models](https://mlsanity.com/paper/2112.10752/) (2021)
- [DeepSeekMath: Pushing the Limits of Mathematical Reasoning in Open Language Models](https://mlsanity.com/paper/2402.03300/) (2024)
- [Scalable Diffusion Models with Transformers](https://mlsanity.com/paper/2212.09748/) (2022)
- [Diffusion Policy: Visuomotor Policy Learning via Action Diffusion](https://mlsanity.com/paper/2303.04137/) (2023)
- [Wan: Open and Advanced Large-Scale Video Generative Models](https://mlsanity.com/paper/2503.20314/) (2025)
- [Stable Video Diffusion: Scaling Latent Video Diffusion Models to Large Datasets](https://mlsanity.com/paper/2311.15127/) (2023)
- [π0: A Vision-Language-Action Flow Model for General Robot Control](https://mlsanity.com/paper/2410.24164/) (2024)
- [Planning with Diffusion for Flexible Behavior Synthesis](https://mlsanity.com/paper/2205.09991/) (2022)
- [NoMaD: Goal Masked Diffusion Policies for Navigation and Exploration](https://mlsanity.com/paper/2310.07896/) (2023)
- [Multi-Robot Motion Planning with Diffusion Models](https://mlsanity.com/paper/2410.03072/) (2024)
- [Simultaneous Multi-Robot Motion Planning with Projected Diffusion Models](https://mlsanity.com/paper/2502.03607/) (2025)
- [Discrete-Guided Diffusion for Scalable and Safe Multi-Robot Motion Planning](https://mlsanity.com/paper/2508.20095/) (2025)
- [Search and Rescue under the Forest Canopy using Multiple UAVs](https://mlsanity.com/paper/1908.10541/) (2019)
- [ImagiNav: Scalable Embodied Navigation via Generative Visual Prediction and Inverse Dynamics](https://mlsanity.com/paper/2603.13833/) (2026)
- [Communication-Aware Multi-robot Coordination with Submodular Maximization](https://mlsanity.com/paper/2011.01476/) (2020)
- [A Cost-Effective Cooperative Exploration and Inspection Strategy for Heterogeneous Aerial System](https://mlsanity.com/paper/2403.01225/) (2024)
- [Realm: Real-Time Line-of-Sight Maintenance in Multi-Robot Navigation with Unknown Obstacles](https://mlsanity.com/paper/2502.15162/) (2025)
- [MARVEL: Multi-Agent Reinforcement Learning for constrained field-of-View multi-robot Exploration in Large-scale environments](https://mlsanity.com/paper/2502.20217/) (2025)
- [Decentralized Simultaneous Multi-target Exploration using a Connected Network of Multiple Robots](https://mlsanity.com/paper/1505.05441/) (2015)
- [Decentralized Global Connectivity Maintenance for Multi-Robot Navigation: A Reinforcement Learning Approach](https://mlsanity.com/paper/2109.08536/) (2021)
- [Graph-based SLAM-Aware Exploration with Prior Topo-Metric Information](https://mlsanity.com/paper/2308.16522/) (2023)
- [Fast k-connectivity Restoration in Multi-Robot Systems for Robust Communication Maintenance](https://mlsanity.com/paper/2404.03834/) (2024)
- [Line-of-Sight-Constrained Multi-Robot Mapless Navigation via Polygonal Visible Regions](https://mlsanity.com/paper/2603.26314/) (2026)

## Abstract (arXiv)

The success of generative models in language and visual generation has inspired extensive applications to generative robot planning. However, most existing works either focus on single-robot planning, or generate multi-robot trajectories in a sequential manner with iterative post-processing to resolve inter-robot conflicts. In this work, we investigate whether coordinated multi-robot trajectories, as a special spatiotemporal distribution, can be learned and generated with a generative model in a feed-forward manner. We propose Robots as Tokens (Roken), a unified diffusion transformer that directly generates multi-robot trajectories that satisfy both (individual) safety and (global) connectivity constraints. The core design of Roken is to represent each robot as a discrete token, allowing them to naturally interact with each other through self-attention, and cross-attend to map tokens for environment layouts. We further introduce several auxiliary tasks based on Bayes' theorem to provide multi-scale spatial-temporal supervision for efficient learning of the conditional distribution. In training, Roken absorbs diverse expert trajectories from different team sizes. During inference, Roken behaves as a versatile multi-robot planner that can handle single-robot planning, coordinated multi-robot trajectory generation, and conditional trajectory generation by fixing some robot tokens as conditions. Experiments in diverse cluttered environments show that Roken can generate coordinated multi-robot trajectories to perform connectivity-constrained goal navigation tasks with high success rates, outperforming the baseline method used to generate the training dataset. Roken also demonstrates good scalability after training with mixed team sizes, and shows generalization to unseen or partially observed environments, verifying its potential to learn from diverse data and perform versatile tasks.