Installation

EMOS CLI

The fastest way to get started with EMOS is through the CLI. Download the latest release:

curl -sSL https://raw.githubusercontent.com/automatika-robotics/emos/main/stack/emos-cli/scripts/install.sh | sudo bash

Or build from source (requires Go 1.23+):

git clone https://github.com/automatika-robotics/emos.git
cd emos/stack/emos-cli
make build
sudo make install

Deployment Modes

EMOS supports two deployment modes. Run emos install without arguments for an interactive menu, or use the --mode flag directly.

No ROS 2 installation required. Runs EMOS inside a Docker container using the public image.

emos install --mode container

You will be prompted to select a ROS 2 distribution (Jazzy, Humble, or Kilted). The CLI pulls the image, creates the container, and sets up the ~/emos/ directory structure.

Requirements: Docker installed and running.

Builds EMOS packages from source and installs them directly into your ROS 2 installation at /opt/ros/{distro}/. No container needed.

emos install --mode native

The CLI will:

  1. Detect your ROS 2 installation

  2. Clone the EMOS source and dependencies into a build workspace (~/emos/ros_ws/)

  3. Install system packages (portaudio, GeographicLib, rmw-zenoh)

  4. Install Python dependencies

  5. Install kompass-core with GPU acceleration support

  6. Build all packages with colcon and install them into /opt/ros/{distro}/

After installation, EMOS packages are available whenever you source ROS2. You can run recipes directly:

source /opt/ros/jazzy/setup.bash
python3 ~/emos/recipes/my_recipe/recipe.py

Requirements: A working ROS2 installation (Humble, Jazzy, or Kilted).

See the CLI Reference for the full list of commands.

Model Serving Platform

EMOS is agnostic to model serving platforms. You need at least one of the following available on your network:

  • Ollama Recommended for local inference.

  • RoboML Automatika’s own model serving layer.

  • OpenAI API-compatible servers e.g. llama.cpp, vLLM, SGLang.

  • LeRobot For Vision-Language-Action (VLA) models.

  • Cloud endpoints HuggingFace Inference Endpoints, OpenAI, etc.

Tip

For larger models, run the serving platform on a GPU-equipped machine on your local network rather than directly on the robot.

Updating

Update your installation to the latest version:

emos update

The CLI detects your installation mode and updates accordingly:

  • Container mode: pulls the latest image and recreates the container.

  • Native mode: pulls the latest source, rebuilds, and re-installs packages into /opt/ros/{distro}/.

Installing from Source (Developer Setup)

If you want to build the full EMOS stack from source for contributing or accessing the latest features, follow the steps below. This installs all three stack components: Sugarcoat (architecture), EmbodiedAgents (intelligence), and Kompass (navigation).

1. Create a unified workspace

mkdir -p emos_ws/src
cd emos_ws/src

2. Clone the stack

git clone https://github.com/automatika-robotics/emos.git
cp -r emos/stack/sugarcoat .
cp -r emos/stack/embodied-agents .
cp -r emos/stack/kompass .

3. Install Python dependencies

PIP_BREAK_SYSTEM_PACKAGES=1 pip install numpy opencv-python-headless 'attrs>=23.2.0' jinja2 httpx setproctitle msgpack msgpack-numpy platformdirs tqdm pyyaml toml websockets

4. Install the Kompass core engine

The kompass-core package provides optimized planning and control algorithms.

For production robots or high-performance simulation, install with GPU acceleration:

curl -sSL https://raw.githubusercontent.com/automatika-robotics/kompass-core/refs/heads/main/build_dependencies/install_gpu.sh | bash

For quick testing or lightweight environments:

pip install kompass-core

5. Install ROS dependencies and build

cd emos_ws
rosdep update
rosdep install -y --from-paths src --ignore-src
colcon build
source install/setup.bash

You now have the complete EMOS stack built and ready to use.