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Quick setup guide

Before going further, make sure you have ROS 2 (Jazzy or Humble) installed and sourced on your system. If you don't have ROS 2 follow the installation documentation for Humble or Jazzy. Make sure that ros-dev-tools are installed.

Docker images

RAI has experimental docker images. See the docker for instructions.

RAI outside of ROS 2

RAI can be used outside of ROS 2. This means that no ROS 2 related features will be available.

You can still use RAI's core agent framework, tool system, message passing, and integrations such as LangChain, even if ROS 2 is not installed or sourced on your machine. This is useful for:

  • Developing and testing AI logic, tools, and workflows independently of any robotics middleware
  • Running RAI agents in simulation or cloud environments where ROS 2 is not present
  • Using RAI as a generic multimodal agent framework for non-robotic applications

If you later decide to integrate with ROS 2, you can simply install and source ROS 2, and all ROS 2-specific RAI features (such as connectors, aggregators, and tools) will become available automatically.

1. Setting up the workspace:

1.1 Install poetry

RAI uses Poetry for python packaging and dependency management. Install poetry with the following line:

curl -sSL https://install.python-poetry.org | python3 -

Alternatively, you can opt to do so by following the official docs.

1.2 Clone the repository:

git clone https://github.com/RobotecAI/rai.git
cd rai

1.3 Create poetry virtual environment and install dependencies:

poetry install
rosdep install --from-paths src --ignore-src -r -y

Additional dependencies

RAI is modular. If you want to use features such as speech-to-speech, simulation and benchmarking suite, openset detection, or NoMaD integration, install additional dependencies:

poetry install --with openset,nomad,s2s,simbench # or `--all-groups` for full setup
Group Name Description Dependencies
s2s Speech-to-Speech functionality rai_asr, rai_tts
simbench Simulation and benchmarking tools rai_sim, rai_bench
openset Open-set detection capabilities groundingdino, groundedsam
nomad Visual Navigation - NoMaD integration visualnav_transformer
docs Documentation-related dependencies mkdocs, mkdocs-material, pymdown-extensions

1.4 Configure RAI

Run the configuration tool to set up your LLM vendor and other settings:

poetry run streamlit run src/rai_core/rai/frontend/configurator.py

Web browser

If the web browser does not open automatically, open the URL displayed in the terminal manually.

2. Build the project:

2.1 Build RAI workspace

colcon build --symlink-install

2.2 Activate the virtual environment:

source ./setup_shell.sh

3. Setting up vendors

RAI is vendor-agnostic. Use the configuration in config.toml to set up your vendor of choice for RAI modules. Vendor choices for RAI and our recommendations are summarized in Vendors Overview.

Best-performing AI models

We strongly recommend you to use of best-performing AI models to get the most out of RAI!

Pick your local solution or service provider and follow one of these guides: