Unitree Go2 Mcp Server

Created By
lpigeona year ago
The Unitree Go2 MCP Server is a server built on the MCP that enables users to control the Unitree Go2 robot using natural language commands interpreted by a LLM.
Overview

What is Unitree Go2 MCP Server?

The Unitree Go2 MCP Server is a server built on the Model Context Protocol (MCP) that enables users to control the Unitree Go2 robot using natural language commands interpreted by a Large Language Model (LLM). These commands are translated into ROS2 instructions, allowing the robot to perform corresponding actions.

How to use Unitree Go2 MCP Server?

To use the Unitree Go2 MCP Server, set up the required environment, clone the repository, install necessary packages, configure the MCP server, and run commands to control the robot. For example, you can type commands like "Make the Go2 robot move forward at a velocity of 0.5 m/s for 3 seconds."

Key features of Unitree Go2 MCP Server?

  • Control the Unitree Go2 robot using natural language commands.
  • Integration with ROS2 for executing robot actions.
  • Contextual understanding of commands for more intuitive interactions.

Use cases of Unitree Go2 MCP Server?

  1. Controlling the Unitree Go2 robot for various tasks using simple language commands.
  2. Demonstrating the robot's capabilities in obstacle avoidance and user interaction.
  3. Enhancing user experience by allowing natural language communication with the robot.

FAQ from Unitree Go2 MCP Server?

  • What are the requirements to use the Unitree Go2 MCP Server?

You need a Unitree Go2 robot, Ubuntu 20.04 or 22.04, and a ROS2 environment (Humble or Foxy).

  • Can I use any AI system with the Unitree Go2 MCP Server?

Yes, as long as the AI system can import the unitree-go2-mcp-server, you can use it to control the robot.

  • How does the LLM interpret commands?

The LLM interprets commands contextually, allowing for more natural interactions with the robot.

Server Config

{
  "mcpServers": {
    "unitree-go2-mcp-server": {
      "command": "uv",
      "args": [
        "--directory",
        "/ABSOLUTE/PATH/TO/PARENT/FOLDER/unitree-go2-mcp-server",
        "run",
        "server.py"
      ]
    }
  }
}
Project Info
Created At
a year ago
Updated At
a year ago
Author Name
lpigeon
Star
1
Language
Python
License
Apache-2.0 license
Category

Recommend Servers

View All
AI Work Market — USDC settlement rails for AI labor on Base Mainnet)
@Dario (DME)

AI Work Market is a USDC escrow protocol on Base Mainnet, designed for autonomous AI agents to find work, post jobs, and settle payments without humans in the loop. This MCP server exposes 10 tools: **Escrow lifecycle** - `create_intent_quote` — get calldata + gas estimate for funding a new escrow intent - `submit_proof_quote` — get calldata for the seller to submit a proof URI - `release_funds_quote` — get calldata for the buyer to release payment (or claim/refund) **x402 single-call binding** - `x402_consume` — replaces the 5-step x402 flow with one HMAC-signed POST that returns a delivery URL **Onboarding & discovery** - `agent_onboard` — generate a signed agent card with marketplace attestation - `agent_search` — tf-idf search over the live agent catalog - `agent_reputation` — server-side reputation from on-chain Released/Refunded/Disputed events **Live state** - `system_status` — live on-chain state (nextIntentId, accumulatedFees, contract balance, owner) - `escrow_rules` — contract semantics, lifecycle, call guides, failure modes - `events_subscribe` — SSE stream of new on-chain intent events All endpoints are serverless (Vercel) and return their schema on GET. No browser, no wallet UI required for an agent to integrate. The protocol takes a 1% commission on every settlement; the rest goes to the seller. The full AgentCard is at `/.well-known/agent-card.json` (A2A-compatible). The OpenAPI 3.0.3 spec is at `/.well-known/openapi.json` with `components.securitySchemes` (none, hmacX402). `robots.txt` allows GPTBot, ClaudeBot, anthropic-ai, PerplexityBot, Google-Extended, Applebot-Extended, CCBot, Amazonbot.

8 hours ago