发表机构
University College London; Carnegie Mellon University（伦敦大学学院; 卡内基梅隆大学）
机构由 AI 辅助整理，请以论文原文为准。AI 中文总结
OpenRUA通过仅提供终端访问ROS 2的零抽象封装，使现成编码智能体在CaP-Bench和LIBERO-PRO上分别达到99.0%和87.0%的成功率，证明其可作为零样本视觉运动策略，无需定制原语或任务特定训练。
AI 中文摘要
编码智能体正在通过编写和执行机器人控制程序将其影响力扩展到物理世界。人们可能期望这些智能体使用工程师们数十年来开发的成熟软件栈来访问传感器和控制运动。然而，先前的工作主要依赖于设计复杂的定制化封装来编排智能体用于机器人操作，特别是通过规定专门的工作流程并提供定制接口。这引出了一个问题：“这种额外的封装工程是否必要？”我们引入了OpenRUA，一个零抽象的封装，它通过仅向现成的编码智能体提供对机器人原生软件接口ROS 2的终端访问，绕过了定制的抽象层。OpenRUA采用极简的工作空间即封装设计，仅提供ROS 2文档和基本工具，同时让编码智能体自行组织工作，而不编排任何智能体工作流。在此工作空间内，OpenRUA将感知重新定义为文件输入/输出，将操作重新定义为编码。借助由Claude Opus 5驱动的Claude Code，OpenRUA在CaP-Bench上达到了99.0%的成功率，在LIBERO-PRO上达到了87.0%的成功率，证明了现成的编码智能体可以通过机器人的原生接口充当零样本视觉运动策略，无需定制原语或任务特定训练。在这种极简设计下，进一步分析揭示了编码智能体的显著涌现行为：（1）在感知方面，智能体在96.80%的回合中自发编写程序来处理原始感官输入并得出度量测量结果。（2）在操作方面，智能体在95.87%的回合中自发构建运动控制客户端（例如，夹爪控制），并在50.13%的回合中构建闭环控制程序（例如，基于传感器反馈调整运动）。我们的代码可在以下网址获取：https URL。
英文摘要
Coding agents are extending their reach into the physical world by writing and executing robot control programs. One might expect the agents to use the existing mature software stack that engineers have developed over decades to access sensors and control motion. Yet prior work primarily engineers complex custom harnesses to orchestrate agents for robot use, particularly by prescribing specialized workflows and providing bespoke interfaces. This raises the question: "Is such additional harness engineering necessary?" We introduce OpenRUA, a zero-abstraction harness that bypasses bespoke abstraction layers by providing off-the-shelf coding agents with only terminal access to the robot's native software interface ROS 2. OpenRUA employs a minimalist workspace-as-harness design, only offering ROS 2 documentation and basic tools while leaving the coding agent to organize its own work without orchestrating any agentic workflow. Within this workspace, OpenRUA recasts perception as file I/O and manipulation as coding. With Claude Code powered by Claude Opus 5, OpenRUA achieves success rates of 99.0% on CaP-Bench and 87.0% on LIBERO-PRO, demonstrating that an off-the-shelf coding agent can serve as a zero-shot visuomotor policy through the robot's native interface, without bespoke primitives or task-specific training. Under this minimalist design, further analysis reveals striking emergent behaviors of coding agents: (1) For perception, the agent spontaneously writes programs that process raw sensory inputs and derive metric measurements in 96.80% of episodes. (2) For manipulation, the agent spontaneously builds motion-control clients (e.g., gripper control) in 95.87% of episodes and closed-loop control programs (e.g., adjusting motion based on sensor feedback) in 50.13% of episodes. Our code is available at https://github.com/terminalworld/OpenRUA.
