Digital Twin Workstation Setup

This guide will provide detailed, step-by-step instructions for setting up a powerful local workstation for digital twin simulation, including NVIDIA driver installation, Docker, and ROS 2 setup.

A robust local workstation is the cornerstone of modern robotics development, especially when dealing with GPU-intensive tasks like photorealistic simulation, synthetic data generation, and training AI models. This guide provides a complete walkthrough for setting up an Ubuntu-based machine to run NVIDIA Isaac Sim and the ROS 2 ecosystem.

1. System Requirements

High-fidelity simulation is computationally expensive. While it's possible to run simpler simulations on lower-end hardware, the following specifications are recommended for a smooth experience with the tools used in this course.

Operating System: Ubuntu 22.04 LTS
CPU: 6-core Intel or AMD processor (or better)
RAM: 32 GB DDR4 (or more)
GPU: NVIDIA RTX Series GPU is mandatory.
- Minimum: NVIDIA RTX 2070 (8 GB VRAM)
- Recommended: NVIDIA RTX 3070 / 4070 (or better) with at least 12 GB VRAM.
Storage: 512 GB NVMe SSD (or larger) for fast loading times.

Warning: An NVIDIA GPU with RTX capabilities is not optional. The simulation tools we will use, like NVIDIA Isaac Sim, rely on real-time ray tracing, which is only available on these cards.

2. Initial System Setup

Start with a fresh installation of Ubuntu 22.04 LTS.

Once installed, open a terminal (Ctrl+Alt+T) and perform a full system update and upgrade. This ensures all your base packages are up-to-date.

sudo apt update
sudo apt-get upgrade -y

Next, install some essential command-line tools.

sudo apt install -y git curl wget vim build-essential

3. NVIDIA Driver Installation

This is the most critical step. A correct NVIDIA driver installation is required for the system to recognize and utilize the GPU.

Check for Recommended Drivers: Ubuntu has a utility to detect your hardware and suggest the appropriate proprietary drivers.
```
ubuntu-drivers devices
```
This will output a list of devices and drivers. Look for the entry corresponding to your NVIDIA GPU. It will likely have a driver : nvidia-driver-XXX - distro non-free (recommended) line.
Install the Recommended Driver: You can either install the specific version number or use the autoinstall command, which is generally the safest option.
```
sudo ubuntu-drivers autoinstall
```
Reboot Your System: A reboot is required to load the new kernel modules for the driver.
```
sudo reboot
```
Verify the Installation: After rebooting, open a terminal and run the NVIDIA System Management Interface (nvidia-smi).
```
nvidia-smi
```
If the installation was successful, you will see a table detailing your GPU name, driver version, and CUDA version. If this command fails, the driver was not installed correctly, and you must debug the issue before proceeding.

4. Docker Engine Installation

We will use Docker to run our simulation environment in a clean, containerized way. This avoids cluttering our host system with dependencies.

Uninstall Old Versions:

for pkg in docker.io docker-doc docker-compose docker-compose-v2 podman-docker containerd runc; do sudo apt-get remove $pkg; done

Set up Docker's apt Repository:

# Add Docker's official GPG key:
sudo apt-get update
sudo apt-get install ca-certificates curl
sudo install -m 0755 -d /etc/apt/keyrings
sudo curl -fsSL https://download.docker.com/linux/ubuntu/gpg -o /etc/apt/keyrings/docker.asc
sudo chmod a+r /etc/apt/keyrings/docker.asc

# Add the repository to Apt sources:
echo \
  "deb [arch=$(dpkg --print-architecture) signed-by=/etc/apt/keyrings/docker.asc] https://download.docker.com/linux/ubuntu \
  $(. /etc/os-release && echo "$VERSION_CODENAME") stable" | \
  sudo tee /etc/apt/sources.list.d/docker.list > /dev/null
sudo apt-get update

Install Docker Packages:

sudo apt-get install -y docker-ce docker-ce-cli containerd.io docker-buildx-plugin docker-compose-plugin

Manage Docker as a Non-Root User: To avoid typing sudo for every Docker command, add your user to the docker group.
```
sudo groupadd docker
sudo usermod -aG docker $USER
```
You will need to log out and log back in for this group membership to take effect.
Verify Docker Installation: After logging back in, open a terminal and run:
```
docker run hello-world
```
This should download and run a small test image, confirming that Docker is working correctly.

5. NVIDIA Container Toolkit Installation

By default, Docker containers cannot access the host's GPU. The NVIDIA Container Toolkit bridges this gap.

Set up the Repository:

curl -fsSL https://nvidia.github.io/libnvidia-container/gpgkey | sudo gpg --dearmor -o /usr/share/keyrings/nvidia-container-toolkit-keyring.gpg \
  && curl -s -L https://nvidia.github.io/libnvidia-container/stable/deb/nvidia-container-toolkit.list | \
    sed 's#deb https://#deb [signed-by=/usr/share/keyrings/nvidia-container-toolkit-keyring.gpg] https://#g' | \
    sudo tee /etc/apt/sources.list.d/nvidia-container-toolkit.list

Install the Toolkit:

sudo apt-get update
sudo apt-get install -y nvidia-container-toolkit

Configure Docker: Restart the Docker daemon to apply the changes.
```
sudo systemctl restart docker
```
Verify GPU Access in Docker: Run a container and execute nvidia-smi from within it.
```
docker run --rm --gpus all nvidia/cuda:12.1.1-base-ubuntu22.04 nvidia-smi
```
This command should output the same nvidia-smi table you saw earlier, but this time it's being generated from inside a Docker container. This confirms that your containers can now leverage the power of your NVIDIA GPU.

6. ROS 2 Humble Hawksbill Installation

Finally, we install ROS 2, the backbone of our robotics software.

Set Locale and Add ROS 2 Repository: Follow the official ROS 2 Humble installation instructions.

sudo apt install software-properties-common
sudo add-apt-repository universe
sudo apt update && sudo apt install curl -y
sudo curl -sSL https://raw.githubusercontent.com/ros/rosdistro/master/ros.key -o /usr/share/keyrings/ros-archive-keyring.gpg
echo "deb [arch=$(dpkg --print-architecture) signed-by=/usr/share/keyrings/ros-archive-keyring.gpg] http://packages.ros.org/ros2/ubuntu $(. /etc/os-release && echo $UBUNTU_CODENAME) main" | sudo tee /etc/apt/sources.list.d/ros2.list > /dev/null

Install ROS 2 Packages: We will install the ros-humble-desktop package, which includes ROS, RViz, demos, and tutorials.
```
sudo apt update
sudo apt install -y ros-humble-desktop
```
Source the Setup File: Add the ROS 2 setup script to your shell's startup file to make ROS 2 commands available in every new terminal.
```
echo "source /opt/ros/humble/setup.bash" >> ~/.bashrc
source ~/.bashrc
```
Verify ROS 2 Installation: Run a simple talker-listener example in two separate terminals.
- Terminal 1: ros2 run demo_nodes_cpp talker
- Terminal 2: ros2 run demo_nodes_py listener
If you see the talker publishing messages and the listener receiving them, your ROS 2 installation is complete.

Your Digital Twin Workstation is now fully configured and ready for the advanced robotics work in the upcoming modules.

1. System Requirements​

2. Initial System Setup​

3. NVIDIA Driver Installation​

4. Docker Engine Installation​

5. NVIDIA Container Toolkit Installation​

6. ROS 2 Humble Hawksbill Installation​