Large Language Models (LLMs) like GPT-4 and Claude are changing how we program robots. Instead of writing detailed step-by-step instructions, you can describe what you want in plain language and let the AI figure out the steps.
What LLMs Enable
Traditional robot programming requires specifying every action in detail. To make a robot "clean the kitchen," you'd need to write code for every sub-task: find the sponge, pick it up, locate dirty dishes, scrub each one...
With LLMs, you describe the goal and the model uses common-sense reasoning to break it down: "First I need to pick up the sponge, then find the soap, then locate dirty dishes..."
This makes robots more flexible and easier to direct—especially for non-programmers who can describe tasks in natural language.
The Safety Challenge
LLMs are powerful but unpredictable. They can "hallucinate" actions that don't make sense, reference capabilities the robot doesn't have, or plan sequences that are physically dangerous.
You would never want an LLM directly controlling motor voltages. The solution is a layered architecture:
The "Sandwich" Model
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Safety Layer (bottom): Deterministic control systems that handle physics—motor limits, collision avoidance, emergency stops. These never change based on LLM output.
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LLM Planner (middle): The reasoning engine that interprets natural language and plans high-level actions. It outputs "pick up the cup" not "set motor 3 to 127."
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Verification Layer (top): Checks the LLM's plan against rules before execution. Is this action allowed? Does the robot have this capability? Is it within the safe operating zone?
The LLM provides flexibility; the layers above and below provide safety.
How Cyberwave Helps
Cyberwave supports LLM integration while maintaining safety guarantees.
Safe Action Execution
LLM-generated plans go through Cyberwave's safety layer before reaching hardware. Even if the LLM plans something impossible or dangerous, the execution layer catches it.
Full Traceability
When something goes wrong, you need to understand why. Cyberwave logs the entire chain:
- What prompt was given
- What plan the LLM generated
- What actions were actually executed
- What the robot observed
This turns "the AI did something weird" into a debuggable software issue.
Guardrails and Policies
Define what actions are allowed in which contexts. An LLM might plan a fast movement, but Cyberwave enforces speed limits based on the operational environment.
The Right Tool for the Job
LLMs excel at high-level reasoning and natural language understanding. They're not meant to replace precise control systems—they're meant to sit on top of them, making robots easier to direct while safety systems keep everything grounded in physical reality.
For implementation details, see the Cyberwave documentation.