MA-VLA：面向协作与组合泛化的多臂视觉-语言-动作模型

摘要

多臂协作正日益成为具身操作中的一项核心能力。近期的视觉-语言-动作（VLA）模型整合了感知、语言与控制，但大多数模型将语言表示为单一的全局指令，并未提供分配和组合各机械臂特定行为的显式机制。这种设计限制了模型向训练中未出现的协作模式的迁移能力。我们提出MA-VLA，一种通过原子动作分配实现多臂协作的统一框架。MA-VLA将协作行为分解为中层原子提示，并将其分配给各机械臂，从而实现显式的子目标指定与跨任务组合复用。为减少对固定执行角色的依赖，我们引入了Arm Shuffle，一种在训练时对各机械臂的观测、状态及所分配原子提示进行置换的方法。该置换机制强制实现角色无关的指令跟随，并支持将行为重组为未见过的协调模式，我们将其称为多臂组合泛化。我们还构建了一个基准，其中测试时的协作模式不存在于训练集中。在仿真与真实世界评估中，此前最先进的VLA模型在这些未见协作场景下大多失败，而MA-VLA始终能够成功。这些结果表明，结构化的逐臂原子动作分配为多臂具身系统中的可扩展泛化提供了一条切实可行的路径。代码、模型和数据可在https://github.com/zhangzaibin/future-robots获取。

English

Multi-arm collaboration is becoming a core capability in embodied manipulation. Recent vision-language-action (VLA) models integrate perception, language, and control, but most represent language as a single global instruction and do not provide an explicit mechanism for assigning and composing arm-specific behaviors. This design limits transfer to collaboration patterns that differ from those observed during training. We present MA-VLA, a unified framework for multi-arm collaboration via atomic action assignment. MA-VLA decomposes cooperative behavior into mid-level atomic prompts and allocates them to individual arms, enabling explicit subgoal specification and compositional reuse across tasks. To reduce reliance on fixed execution roles, we introduce Arm Shuffle, a training-time permutation of the observation, state, and assigned atomic prompts for each arm. This permutation enforces role-agnostic instruction following and supports recomposition into unseen coordination patterns, which we term multi-arm compositional generalization. We also construct a benchmark in which test-time collaboration patterns are absent in training set. Across simulation and real-world evaluations, prior state-of-the-art VLAs largely fail under these unseen collaborations, while MA-VLA consistently succeeds. These results indicate that structured, per-arm atomic action assignment offers a practical route to scalable generalization in multi-arm embodied systems. Code, models, and data are available at https://github.com/zhangzaibin/future-robots