Many robotic platforms expose an API through which external software can command their actuators and read their sensors. However, transitioning from these low-level interfaces to high-level autonomous behaviour requires a complicated pipeline, whose components demand distinct areas of expertise. Existing approaches to bridging this gap either require retraining for every new embodiment or have only been validated across structurally similar platforms. We introduce RACAS (Robot-Agnostic Control via Agentic Systems), a cooperative agentic architecture in which three LLM/VLM-based modules (Monitors, a Controller, and a Memory Curator) communicate exclusively through natural language to provide closed-loop robot control. RACAS requires only a natural language description of the robot, a definition of available actions, and a task specification; no source code, model weights, or reward functions need to be modified to move between platforms. We evaluate RACAS on several tasks using a wheeled ground robot, a recently published novel multi-jointed robotic limb, and an underwater vehicle. RACAS consistently solved all assigned tasks across these radically different platforms, demonstrating the potential of agentic AI to substantially reduce the barrier to prototyping robotic solutions.

翻译：许多机器人平台通过API提供外部软件控制其执行器与读取传感器数据的接口。然而，从这些底层接口过渡到高层自主行为需要复杂的处理流程，其各组件要求不同的专业知识领域。现有填补这一空白的方法要么需要为每个新实体重新训练模型，要么仅在结构相似的平台上得到验证。我们提出RACAS（通过智能体系统实现机器人无关控制），这是一种协作式智能体架构，其中三个基于LLM/VLM的模块（监控器、控制器与记忆管理器）完全通过自然语言进行通信，以实现闭环机器人控制。RACAS仅需机器人的自然语言描述、可用动作定义及任务规范；在平台间迁移时无需修改源代码、模型权重或奖励函数。我们在轮式地面机器人、近期发布的新型多关节机械臂以及水下航行器上对RACAS进行了多项任务评估。RACAS在这些结构迥异的平台上持续完成了所有指定任务，证明了智能体人工智能在显著降低机器人解决方案原型开发门槛方面的潜力。