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.

翻译：生成模型在语言和视觉生成领域的成功，启发了其在生成式机器人规划中的广泛应用。然而，现有工作大多聚焦于单机器人规划，或以序贯方式通过迭代后处理解决机器人间冲突来生成多机器人轨迹。本文探究能否将协调多机器人轨迹这一特殊时空分布通过生成模型以前馈方式学习并生成。我们提出"机器人即Token"（Roken）方法——一种统一扩散Transformer，能直接生成同时满足（个体）安全性与（全局）连通性约束的多机器人轨迹。其核心设计是将每个机器人表征为离散Token，使其通过自注意力机制自然交互，并通过交叉注意力机制关联地图Token以感知环境布局。我们进一步基于贝叶斯定理引入若干辅助任务，为条件分布的高效学习提供多尺度时空监督。训练时，Roken可吸收不同团队规模下的多样化专家轨迹；推理时，Roken可作为通用多机器人规划器，支持单机器人规划、协调多机器人轨迹生成，以及通过固定部分机器人Token作为条件实现条件轨迹生成。在多种杂乱环境中的实验表明，Roken能生成协调多机器人轨迹，以高成功率完成连通性约束目标导航任务，性能超越用于生成训练数据集的基线方法。Roken在混合团队规模训练后展现出良好可扩展性，并具备对未见或部分观测环境的泛化能力，验证其从多样化数据中学习并执行多类任务的潜力。