Robo-Ops: AI-Driven Robotics Workflow

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  GitHub Repository: Clean code structure for Robo-Ops AI Orchestrator with refined manual JSON response handling.

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  Successful Execution: Terminal output showing the structured JSON task generated from natural language command.

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  Robo-Ops: An AI-driven engine that translates human language into robotic execution plans using Google Gemini

Inspiration

Robotics projects are often complex and difficult to manage for non-technical users. We were inspired to create a bridge between human language and robotic actions, making robot orchestration as simple as sending a text message.

What it does

Robo-Ops AI is a logic engine that takes natural language commands (like "Go to the kitchen and bring water") and converts them into structured robotic execution plans (JSON format). It uses the power of Google Gemini to understand context and plan multi-step tasks.

How we built it

• GitLab: Used for hosting the project framework and managing the development workflow.
• Google Gemini (via Google AI Studio): Used as the primary AI engine to process and translate human instructions.
• JSON: Used as the standardized output format to communicate with robotic systems.

Challenges we faced

The biggest challenge was ensuring the AI consistently produces valid JSON outputs that a robot can understand without errors. We overcame this by using precise system prompting and iterative testing in Google AI Studio.

What we learned

We learned how to leverage Large Language Models (LLMs) to handle complex logical sequencing and how to integrate cloud-based AI with traditional dev workflows.

What's next for Robo-Ops

We plan to integrate this with real-world robotic simulators like ROS or Gazebo and implement real-time command execution directly from GitLab Issues.

Built With

• ai-agent
• gitlab
• google-ai-stdio
• google-gemini
• json
• pytho
• robotics