mlsanityData to 7 Oct 2026

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

arXiv 2605.09055 ↗First posted 9 May 2026cs.RO cs.AI cs.MAcited by 0

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Abstract (arXiv)

Bringing a previously unintegrated device under the control of an AI agent still requires device-specific engineering: driver selection, dependency resolution, interface design, and deployment, repeated per device and per platform. We present Octopus, a hardware onboarding framework in which a coding agent, rather than a shipped integration, is the runtime that produces the required infrastructure. Given shell access and a model API key, a single bootstrap command drives the agent through a five-stage pipeline that enumerates operating-system- visible hardware, infers device identity and capabilities, generates typed Model Context Protocol tools and the hardware-facing code behind them, and activates the result as a live endpoint. A persistent daemon then maintains the result, repairing defined classes of failure in the deployment it produced. Across four hosts spanning two processor architectures, three operating- system families, and two device-access paths, identical prose specifications produced working interfaces with no per-host edits and no hand-written integration code. Five consecutive runs on the reference host completed end to end on first attempt. We report both the resulting capability and a failure mode of unattended repair loops observed over eleven hours of continuous operation.

Key facts. No citing papers in the map yet (data through 7 Oct 2026). Builds on 23 of its 30 references that are in the map. First posted on arXiv 9 May 2026.

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23 of 30 references.

References · 23

  1. 2023RT-2: Vision-Language-Action Models Transfer Web Knowledge to Robotic Controlcited by 2,993
  2. 2024OpenVLA: An Open-Source Vision-Language-Action Modelcited by 2,777
  3. 2022Do As I Can, Not As I Say: Grounding Language in Robotic Affordancescited by 2,302
  4. 2024SWE-agent: Agent-Computer Interfaces Enable Automated Software Engineeringcited by 1,628
  5. 2023MemGPT: Towards LLMs as Operating Systemscited by 1,295
  6. 2022Code as Policies: Language Model Programs for Embodied Controlcited by 1,172
  7. 2025GR00T N1: An Open Foundation Model for Generalist Humanoid Robotscited by 1,167
  8. 2024RepairAgent: An Autonomous, LLM-Based Agent for Program Repaircited by 225
  9. 2022Robot Operating System 2: Design, Architecture, and Uses In The Wildcited by 190
  10. 2024AIOS: LLM Agent Operating Systemcited by 94
  11. 2014Reducing the Barrier to Entry of Complex Robotic Software: a MoveIt! Case Studycited by 86
  12. 2026CaP-X: A Framework for Benchmarking and Improving Coding Agents for Robot Manipulationcited by 72
11 more
  1. 2026RoboClaw: An Agentic Framework for Scalable Long-Horizon Robotic Taskscited by 30
  2. 2024Specifications: The missing link to making the development of LLM systems an engineering disciplinecited by 17
  3. 2024Enabling Novel Mission Operations and Interactions with ROSA: The Robot Operating System Agentcited by 16
  4. 2025From REST to MCP: An Empirical Study of API Wrapping and Automated Server Generation for LLM Agentscited by 14
  5. 2025IoT-MCP: Bridging LLMs and IoT Systems Through Model Context Protocolcited by 9
  6. 2024Self-Healing Machine Learning: A Framework for Autonomous Adaptation in Real-World Environmentscited by 8
  7. 2026TerraFormer: Automated Infrastructure-as-Code with LLMs Fine-Tuned via Policy-Guided Verifier Feedbackcited by 4
  8. 2025RoboNeuron: A Middle-Layer Infrastructure for Agent-Driven Orchestration in Embodied AIcited by 3
  9. 2026Skilled AI Agents for Embedded and IoT Systems Developmentcited by 3
  10. 2026ROSClaw: An OpenClaw ROS 2 Framework for Agentic Robot Control and Interactioncited by 3
  11. 2025Robot Context Protocol (RCP): A Runtime-Agnostic Interface for Agent-Aware Robot Controlcited by 1

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