ROS-LLM

The ROS-LLM project is a ROS framework for embodied intelligence applications. It enables natural language interactions and large model-based control of robot motion and navigation for any robot operating on ROS.

ROS-LLM empowers you to utilize functionalities based on Large Language Models, such as GPT-4 and ChatGPT, for robot decision-making and control.

This framework is designed to be easy to extend. By simply providing a function interface for your robot, following the provided example, you can integrate and use ROS-LLM within ten minutes.

ROS-LLM offers a simple solution for quickly creating interactive and control experiences with any robot.

🚀 Features

• 🤖 ROS Integration: Smoothly interacts with the Robot Operating System (ROS) for expansive robotic control.

• 🧠 Large Language Models Support: Leverages GPT-4 and ChatGPT for enhanced decision-making and task management.

• 🗣️ Natural Interaction: Facilitates intuitive communication with robots through conversational engagement.

• 🔄 Flexible Control: Utilizes LLM-based systems for tasks such as motion and navigation based on language model interpretation.

• 🔌 Simplified Extensibility: Provides an easy interface for seamless robot function integration.

• 🛠️ Quick Development: Creates interactive robot control experiences swiftly, sometimes in under ten minutes.

• 📚 Instructional Examples: Offers comprehensive tutorials and examples for easier understanding and implementation.

• 🗃️ History Storage: Retains local chat histories for convenient review and reference.

🔥 Quickstart Guide

Follow the instructions below to set up ROS-LLM:

1. Clone the Repository:

Use the command below to clone the repository.

git clone https://github.com/Auromix/ROS-LLM.git

2. Install Dependencies:

Navigate to the llm_install directory and execute the installation script.

cd ROS-LLM/llm_install
bash dependencies_install.sh

3. Configure OpenAI Settings:

If you don’t have an OpenAI API key, you can obtain one from OpenAI Platform. Use the script below to configure your OpenAI API key.

cd ROS-LLM/llm_install
bash config_openai_api_key.sh

4. Configure AWS Settings (Optional):

For cloud natural interaction capabilities, configure the AWS settings. If you prefer to use local ASR, this step can be skipped.

For low-performance edge embedded platforms, it is recommended to use ASR cloud services to reduce computing pressure, and for high-performance personal hosts, it is recommended to use local ASR services to speed up response

cd ROS-LLM/llm_install
bash config_aws.sh

4. Configure OpenAI Whisper Settings (Optional):

For local natural interaction capabilities, configure the OpenAI Whisper settings. If you prefer to use cloud ASR, this step can be skipped.

For low-performance edge embedded platforms, it is recommended to use ASR cloud services to reduce computing pressure, and for high-performance personal hosts, it is recommended to use local ASR services to speed up response

pip install -U openai-whisper
pip install setuptools-rust

5. Build the Workspace:

Navigate to your workspace directory and build the workspace.

cd <your_ws>
rosdep install --from-paths src --ignore-src -r -y  # Install dependencies
colcon build --symlink-install

6. Run the Demo:

Source the setup script and launch the Turtlesim demo with cloud ASR.

source <your_ws>/install/setup.bash
ros2 launch llm_bringup chatgpt_with_turtle_robot.launch.py

start listening

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Contributing to ROS-LLM Project

Thank you for your interest in contributing to ROS-LLM Project! Here are some guidelines to help you get started.

Reporting Bugs

If you find a bug in the project, please open an issue on GitHub and provide as much detail as possible about the problem, including steps to reproduce it.

Suggesting Features

If you have an idea for a new feature or improvement to the project, please open an issue on GitHub and describe your suggestion in detail.

Submitting Code Changes

If you want to contribute code changes to the project, please follow these steps:

1. Fork the repository on GitHub.
2. Create a new branch for your changes.
3. Make your changes and commit them to your branch.
4. Push your branch to your fork on GitHub.
5. Open a pull request on the main repository and describe your changes in detail.

We appreciate all contributions to ROS-LLM Project and will review pull requests as quickly as possible. Thank you for your help!