一个AI系统能通用控制多种机器人，无需改代码或重新训练。
RACAS: Controlling Diverse Robots With a Single Agentic System
- 用三个基于大模型的模块通过自然语言协作实现闭环控制。
- 在轮式机器人、新型机械臂和水下机器人上均成功完成任务。
- 只需提供机器人描述和动作定义，适合快速原型开发人员。
许多机器人平台通过API提供对执行器和传感器的低级控制接口，但将其转化为高层自主行为需要复杂的流水线，且各环节需不同专业技能。现有方法要么需为每个新平台重新训练，要么仅在结构相似平台上验证过。我们提出RACAS（基于代理系统的机器人无关控制），采用三个基于LLM/VLM的模块（监控器、控制器、记忆管理员）仅通过自然语言通信，实现闭环机器人控制。RACAS仅需机器人的自然语言描述、可用动作定义和任务说明，无需修改源码、模型权重或奖励函数即可跨平台使用。我们在轮式地面机器人、近期发布的新型多关节机械臂和水下无人艇上评估RACAS，其在所有任务中均成功运行，证明了代理式AI可显著降低机器人原型开发门槛。
原文摘要 · Abstract (English)
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.
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