Revolutionizing Swarm Robotics with Natural Language Commands "Minions"

This project explores the integration of Large Language Models (LLMs) and Behavior Trees (BTs) to develop an intuitive system, Minions, for controlling swarm robotics using natural language commands. The research aims to bridge the gap between non-expert users and complex swarm systems by translating natural language instructions into executable actions.

Project overview

This project explores the integration of Large Language Models (LLMs) with Behavior Trees (BTs) to enable intuitive control of swarm robotics through natural language commands. Introducing “Minions,” the system translates human instructions into executable actions for robotic swarms. It addresses key challenges in swarm robotics, such as the complexity of programming multiple agents and the need for user-friendly interfaces for non-experts.

The methodology involves prompt engineering, synthetic dataset creation, and fine-tuning state-of-the-art models like LLAMA-3, Mistral, and WestLake. Evaluated using ROUGE-L scores, success ratios, and syntactic correctness, experiments in the Violet simulator show significant improvements in generating accurate and executable behavior trees. The results indicate that fine-tuned models perform exceptionally well in both zero-shot and one-shot scenarios, enhancing the accessibility and usability of swarm robotics. Potential applications include search and rescue, agricultural monitoring, and environmental sensing.

Project Gool

The primary goal of this project is to develop an intuitive system that enables effective control of swarm robotics through natural language commands. By leveraging advanced LLMs and BTs, the project aims to simplify the programming and coordination of robotic swarms, making the technology accessible to non-expert users and expanding its applications in various fields.

How It Works

Minions is a cutting-edge system that enables intuitive control of robotic swarms using natural language commands.  Minions translates human instructions into executable actions for robotic swarms, simplifying the complexity of programming and coordinating multiple agents. This innovative approach makes swarm robotics accessible to non-experts, enhancing usability and expanding potential applications in fields such as search and rescue, agricultural monitoring, and environmental sensing.

Use Cases

Search and Rescue Operations: Rescuers can use natural language commands like “Search the building for survivors and bring them to the exit” to direct the swarm. The robots can efficiently cover large areas, navigate through debris, and communicate findings, significantly speeding up rescue operations.

Agricultural Monitoring: Farmers can issue commands such as “Inspect the north field for pest infestations and report back” or “Monitor soil moisture levels and adjust irrigation as needed.” The robotic swarm can autonomously perform these tasks, providing real-time data and insights, thus improving crop management and yield.

Warehouse Automation: Warehouse managers can command the robots with instructions such as “Locate and retrieve item X from shelf Y and deliver it to packing station Z.” The robotic swarm can navigate the warehouse, manage inventory, and handle logistics, streamlining operations and reducing human labor.

 

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