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Octopus Protocol: One-Shot Hardware Discovery and Control for AI Agents via Infrastructure-as-Prompts

Authors
Quilee Simeon, Justin M. Wei, Yile Fan
Source
arXiv
Published
Collection
Embodied AI
Hardware DiscoveryMCP ToolsRobot ControlAgentic RoboticsSelf Healing Systems
Open arXivMarkdown source

Summary

Octopus Protocol uses a coding agent to discover connected hardware, generate MCP tools, and deploy a live hardware server from one bootstrap command. The paper targets the driver and SDK work that blocks agents from controlling new devices.

Problem

  • Agentic robotics systems such as Code-as-Policies and VLA policies usually assume existing ROS nodes, SDKs, drivers, or action APIs.
  • New hardware bring-up still requires humans to inspect devices, write glue code, install dependencies, expose control primitives, and repair failures.
  • This matters because hardware integration can dominate the engineering cost before an AI agent can act in the physical world.

Approach

  • The system runs a five-stage pipeline: probe OS devices, identify capabilities, generate typed MCP tool schemas, write a FastMCP server, then deploy an HTTP/SSE endpoint.
  • A coding agent compiles markdown hardware specifications and live probe results into platform-specific driver code at setup time.
  • The generated tools can include actions such as set_servo_angle and capture_image, with typed inputs exposed to any MCP-compliant client.
  • A persistent daemon watches logs, repairs generated code or dependencies after failures, and uses generated camera tools to summarize physical state.

Results

  • One bootstrap command onboarded hardware in about 10-15 minutes and exposed up to 30 MCP tools, depending on the detected platform and devices.
  • The same markdown specification worked on 3 hosts: Windows/WSL PC, Apple Silicon macOS, and Raspberry Pi 4.
  • The Raspberry Pi setup typically exposed about 18 tools; the Mac setup reached close to the 30-tool cap.
  • On a benchtop SO-ARM101 6-DOF arm with USB camera feedback, an MCP client performed closed-loop visual-motor control by capturing an image, observing pose, moving a joint, and verifying with a second capture.
  • The self-healing stage handled 3 induced failures: missing Python dependency, USB arm hot-unplug/replug, and deliberate generated-server corruption.
  • The orchestrator passed 14 of 14 integration tests and used about 640 lines of Python plus about 560 lines of markdown specifications; the runtime MCP server was generated during setup.

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