OpenRUA: Robot-Use Agents Are Zero-Shot Visuomotor Policies

2026-10-01

AI Summary

This study addresses the bottleneck of robotic agents relying on complex, customized frameworks by proposing a zero-abstraction framework that enables general-purpose coding agents to directly control robots solely through a ROS 2 terminal interface. This approach maps perception to file I/O and manipulation to programming tasks, achieving zero-shot visuomotor policies without custom primitives or task-level training. Notably, it reveals emergent behaviors wherein agents spontaneously write perception and control code. Built upon Claude Opus and a minimalist workspace design, the proposed method achieves success rates of 99.0% and 87.0% on the CaP-Bench and LIBERO-PRO benchmarks, respectively.

Abstract

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.

Problem

• coding agents
• robot control
• zero-shot visuomotor policy
• ROS 2
• harness engineering

Innovation

• Zero-shot Visuomotor Policy
• Zero-abstraction Harness
• ROS 2
• Coding Agents
• Workspace-as-harness