ROS MCP Server

# ROS MCP Server 🧠⇄🤖        <!-- mcp-name: io.github.robotmcp/ros-mcp-server --> <p align="center"> <img src="https://github.com/robotmcp/ros-mcp-server/blob/main/docs/images/framework.png"/> </p> ROS-MCP-Server connects large language models (such as Claude, GPT, and Gemini) with existing robots giving them bidirectional AI integration. With no changes to existing robot source code, this enables: - 🗣 **Commanding the robot in natural language** → instructions are translated into ROS/ROS2 commands. - 👀 **Giving AI full visibility** → subscribe to topics, call services, read sensor data, and monitor robot state in real time. ### ✅ Key Benefits - **No robot code changes** → only requires adding the `rosbridge` node. - **True two-way communication** → LLMs can both *control* robots and *observe* everything happening in ROS (sensors, topics, parameters). - **ROS1 & ROS2 support** → works with both versions out of the box. - **MCP-compatible** → integrates with any MCP-enabled LLM (Claude Desktop, Gemini, ChatGPT, and beyond). ## 🎥 Examples in Action 🖥️ **Example - Controlling the MOCA mobile manipulator in NVIDIA Isaac Sim** Commands are entered into Claude Desktop, which uses the MCP server to directly drive the simulated robot. <p align="center"> <img src="https://github.com/robotmcp/ros-mcp-server/blob/main/docs/images/result.gif" /> </p> --- 🐕 **Example - Controlling Unitree Go with natural language** ([video](https://youtu.be/RW9_FgfxWzs?si=8bdhpHNYaupzi9q3)) The MCP server enables the Claude to interpret images from the robot's cameras, and then command the robot based on human natural language commands. <p align="left"> <img src="https://contoro.com/asset/media/demo_go2.gif" /> </p> --- 🏭 **Example - Debugging an industrial robot** ([Video](https://youtu.be/SrHzC5InJDA)) - Connecting to an industrial robot enables the LLM to browse all ROS topics and services to assess the robot state. - With no predefined context, the MCP server enables the LLM to query details about custom topic and service types and their syntax (00:28). - Using only natural language, the operator calls the custom services to test and debug the robot(01:42). <p align="center"> <a href="https://contoroinc.sharepoint.com/:v:/s/SandboxNewBusiness/EVh2t2_YG9BEl-Bw-8k6xucBcEv7XebJv1MtqLTIfrQpig?e=deu3YO"> <img src="https://github.com/robotmcp/ros-mcp-server/blob/main/docs/images/Contoro_robot.png" width="400" alt="Testing and debugging an industrial robot" /> </a> </p> --- ## ⚙️ Features of the ROS MCP Server - **List topics, services, and message types** → explore everything available in your robot’s ROS environment. - **View type definitions (incl. custom)** → understand the structure of any message. - **Publish/subscribe to topics** → send commands or stream robot data in real time. - **Call services (incl. custom)** → trigger robot functions directly. - **Get/set parameters** → read or adjust robot settings on the fly. - 🔜 **Action support** → upcoming support for ROS Actions. - 🔜 **Permission controls** → manage access for safer deployments. --- ## 🛠 Getting Started The MCP server is version-agnostic (ROS1 or ROS2) and works with any MCP-compatible LLM. <p align="center"> <img src="https://github.com/robotmcp/ros-mcp-server/blob/main/docs/images/MCP_topology.png"/> </p> ### Installation Follow the [installation guide](docs/installation.md) for step-by-step instructions: 1. Clone the repository 2. Install `uv` and `rosbridge` 3. Install Claude Desktop (or any MCP-enabled client) 4. Configure your client to connect to the ROS MCP Server 5. Start `rosbridge` on the target robot --- ## 📚 More Examples & Tutorials Browse our [examples](examples) to see the server in action. We welcome community PRs with new examples and integrations! --- ## 🤝 Contributing We love contributions of all kinds: - Bug fixes and documentation updates - New features (e.g., Action support, permissions) - Additional examples and tutorials Check out the [contributing guidelines](docs/contributing.md) and see issues tagged **good first issue** to get started. --- ## 📜 License This project is licensed under the [Apache License 2.0](LICENSE).