Docker Environment

Purpose

This repository uses Docker to provide an isolated and consistent development environment for the pyicub system. The environment ensures reproducibility, avoids system pollution, and streamlines the onboarding process. It supports modular builds for the robotics backend (including robotology-superbuild and pyicub) and the frontend, all orchestrated through Docker Compose.

Benefits of Containerization

Reproducibility: All developers and CI runners use the exact same environment.
Simplified Setup: No need to manually install system packages or Python dependencies.
Safe Experimentation: Test upgrades or changes in isolated containers.
Multi-profile Workflows: Selectively run services (e.g. backend only, frontend only, or test pipelines).

Environment Structure

backend/: Dockerfile for simulation & core robotics stack.
frontend/: Dockerfile for frontend service (optional, e.g., Web Interface for using the robot).
compose.yaml: Manages service orchestration via Docker Compose.
.env: Defines global project variables and Compose profile selection.
scripts/: Contains helper scripts like runSetup.sh, runTests.sh, and automation logic.

The Role of `.env` and `COMPOSE_PROFILES`

`.env`

The .env file centralizes environment variables used across Docker Compose. This includes:

Image tags (PYICUB_IMAGE_NAME, ROBOTOLOGY_IMAGE_NAME)
Profile flags (COMPOSE_PROFILES)
YARP/ICUB Settings (ICUB_SIMULATION, ICUBSRV_HOST, etc.)
Feature toggles for Pyicub modules (PYICUB_API, logging paths, ports)

It is injected automatically into each service using the env_file: directive in Compose and is referenced both at build time (e.g., build.args) and runtime (environment: block).

`COMPOSE_PROFILES`

docker compose supports profiles for conditional service inclusion. This project defines three:

Profile	Services Included	Purpose
`backend`	`pyicub`	Main development and simulation container
`frontend`	`pyicub-frontend`	UI layer
`test`	`pyicub-test`	Executes test suite in isolated container

By setting COMPOSE_PROFILES, you choose which parts of the system to bring up.

Build and Run Workflow

Using the `go` script (recommended)

This script automates the build and run process (host machine):

./docker/go

What it does: 1. Sets up XDG_RUNTIME_DIR if unset. 2. Grants X server access to root inside the container (xhost +local:root). 3. Runs docker compose down with COMPOSE_PROFILES=backend,frontend (clean start). 4. Builds services (backend, frontend) using docker compose build. 5. Starts up the services (docker compose up). 6. Brings them down after exit (if not detached).

Note: You can modify COMPOSE_PROFILES in the script to suit your development needs.

Manual Usage

Step 1 – Build

COMPOSE_PROFILES=backend docker compose build

Step 2 – Run

COMPOSE_PROFILES=backend docker compose up

Use -d for detached mode:

COMPOSE_PROFILES=backend docker compose up -d

Step 3 – Tear Down

docker compose down --remove-orphans

Developing Inside the Container

Default Workspace Structure

Upon container startup, a terminator terminal is launched and you are placed inside /workspace, which includes:

/workspace/
├── icub-apps/
├── pyicub/
├── pyicub-apps/
└── scripts/

``icub-apps/`` – YARP-based and Gazebo applications and configurations used in simulation.
``pyicub/`` – Python codebase of the pyicub library.
``pyicub-apps/`` – Additional Python robotic applications.
``scripts/`` – Entry-point and automation scripts for setup, testing, and environment configuration (e.g., runSetup.sh, runTests.sh).

This structure is mounted from a Compose volume and persists as long as the volume exists.

Setup and Initialization

The container entrypoint runs:

/opt/scripts/runSetup.sh

This script: - Prepares device simulation and YARP configuration. - Initializes any required services before interaction.

Running Tests

A dedicated test profile is available for automated test execution.

To launch the tests via Compose:

COMPOSE_PROFILES=test docker compose up

Alternatively, from within the running container:

From yarpmanager > Applications > iCub_Gazebo, run all the scripts displayed.
Then run:
```
pytest -v /workspace/pyicub/tests
```

This command produces CLI test output and logs.

Best Practices

Use test profile for isolated, fast validation pipelines.
Mount volumes to persist workspace data between sessions.
Use go for streamlined build/run/down workflow.
Do all code execution and editing inside /workspace for clarity and compatibility.
The container runs as root intentionally to simplify permission handling for devices like YARP ports or Gazebo simulation.