Unitree Go2 MCP Server

The Unitree Go2 MCP Server bridges the gap between natural language interfaces and autonomous quadruped robotics. By exposing a set of Model Context Protocol (MCP) functions that translate plain‑English commands into ROS 2 messages, it lets developers and end users control the Unitree Go 2 robot without writing code. This is especially valuable in research labs, rapid prototyping environments, and educational settings where the overhead of ROS programming can be a barrier to entry. Instead of crafting intricate topic subscriptions or writing custom drivers, an LLM‑powered assistant can interpret user intent and issue the appropriate ROS commands on behalf of the operator.

At its core, the server offers a catalog of high‑level robot actions—such as moving forward at a specified velocity, turning, stopping, or performing predefined gaits—alongside lower‑level configuration tweaks. Each action is exposed as an MCP function, complete with parameter validation and error handling that maps directly to the underlying ROS 2 services or topics. The server also supports optional rosbridge integration, allowing remote clients to communicate over WebSockets when the robot is not on a shared network. This dual‑mode connectivity ensures that the Go 2 can be controlled from desktop LLMs, mobile apps, or even web dashboards.

Key features include:

• Natural‑language to ROS translation: LLMs parse user input and invoke the correct MCP function with inferred parameters.
• ROS 2 compatibility: Works seamlessly with both Foxy and Humble distributions, leveraging the official Unitree ROS 2 package.
• Network flexibility: Supports direct ROS 2 communication or rosbridge, enabling control over LAN or the internet.
• Extensible function list: The MCPFUNCTIONS.md file outlines all available commands, making it easy to add new capabilities or customize existing ones.
• Real‑time feedback: The server can return status messages and telemetry, allowing the LLM to provide contextual updates to the user.

Typical use cases span from simple demonstrations—“make the robot walk forward for 3 seconds”—to complex task orchestration, such as navigating a cluttered environment or performing payload delivery. In research, the server can serve as a testbed for human‑robot interaction studies, where participants issue commands in natural language and the robot responds immediately. In industry, it can accelerate field testing of autonomous patrol robots by allowing operators to issue high‑level directives without deep ROS knowledge.

Integrating the Unitree Go 2 MCP Server into an AI workflow is straightforward: once the server is running on a machine connected to the robot, any LLM‑enabled assistant that supports MCP can import it as a tool. The assistant then calls the relevant function, passing user‑derived parameters, and receives a structured response. This tight coupling means developers can focus on higher‑level reasoning—planning, safety checks, or multimodal perception—while the MCP server handles low‑level motion execution. The result is a powerful, developer‑friendly bridge that unlocks the full potential of autonomous quadruped robotics through conversational AI.