Deploy OpenClaw inside Docker containers for reproducible, portable robotics development. This guide covers single-container setups, multi-service docker-compose stacks, GPU passthrough for simulation, and production image hardening.
Pull and run the official image with: docker pull openclaw/openclaw:latest && docker run -it --rm -p 7400:7400 openclaw/openclaw:latest openclaw sim --demo. This launches a simulated UR5e arm in the built-in physics simulator within seconds. For GPU-accelerated simulation add --gpus all to the docker run command.
Use host networking mode (--network host) or expose the robot port explicitly (-p 30002:30002 for Universal Robots). Mount the robot config file: -v ./robot.yaml:/openclaw/config/robot.yaml. Start with: docker run --network host -v ./robot.yaml:/openclaw/config/robot.yaml openclaw/openclaw:latest openclaw run --config /openclaw/config/robot.yaml.
Yes. Docker Desktop for Windows and macOS runs OpenClaw containers transparently. Note that on macOS and Windows, GPU passthrough requires additional configuration (WSL2 GPU support on Windows; macOS Metal GPU passthrough is not yet supported in Docker Desktop for robotics simulation). CPU-only simulation works on all platforms.
Start from the official base image: FROM openclaw/openclaw:3.1-runtime. Copy your plugin shared library into /openclaw/plugins/. Copy your config file to /openclaw/config/. Run RUN openclaw plugin install --scan /openclaw/plugins/ to register the plugin. Build and tag: docker build -t my-org/openclaw-custom:1.0 . Then test with docker run my-org/openclaw-custom:1.0 openclaw plugin list.
Use a three-service compose file: the openclaw/openclaw:latest service exposes port 7400; the ros:humble service sources /opt/ros/humble/setup.bash and runs the openclaw_ros2 bridge; a redis:7-alpine service stores shared state. Mount a shared volume between OpenClaw and ROS 2 services for log and config sharing. Set ROS_DOMAIN_ID as an environment variable on both ROS 2 services.
Define named volumes in docker-compose.yml: openclaw_config: and openclaw_logs:. Mount them in the service: volumes: - openclaw_config:/openclaw/config - openclaw_logs:/openclaw/logs. Named volumes survive container removal (docker rm) but are removed by docker-compose down -v, so use docker-compose down without -v for maintenance restarts.
Install the NVIDIA Container Toolkit on the host, then add to docker run: --gpus all --runtime=nvidia. In docker-compose.yml, set: deploy: resources: reservations: devices: - capabilities: [gpu]. Verify GPU access inside the container with: openclaw sim --check-gpu. Bullet physics simulation performance scales linearly with GPU capability.
The :runtime image (~800 MB) includes only production runtime files, shared libraries, and the CLI. The :dev image (~2.4 GB) adds C++ headers, CMake, GCC, Python development packages, and testing tools needed to build plugins or the SDK from source. Use :runtime in production and :dev for development environments where you are writing custom drivers or plugins.
Update by pulling the new image tag: docker pull openclaw/openclaw:3.2. Update your docker-compose.yml image field to the new tag and run docker-compose up -d. Containers are stateless, so configuration changes should be managed through mounted volumes rather than inside the container to ensure updates are non-destructive.
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