Docker - RLRoverLab Documentation

Installation Using Docker

Docker is the recommended installation method as it provides a consistent environment with all dependencies pre-installed.

The image is pinned to Isaac Sim 6.0.1 (including its multi-architecture image digest), Isaac Lab v3.0.0-beta2.patch1 (ffff603), and Python 3.12.

Prerequisites

.Xauthority for graphical access: Run the following command to verify or create .Xauthority.

[ ! -f ~/.Xauthority ] && touch ~/.Xauthority && echo ".Xauthority created" || echo ".Xauthority already exists"

Nvidia Container Toolkit: see nvidia-container-toolkit

After installing the toolkit remember to configure the container runtime for docker using
```
sudo nvidia-ctk runtime configure --runtime=docker
sudo systemctl restart docker
```
You may need to allow docker to access X server if you want to use the GUI:
```
xhost +local:docker
```
Login to NGC
1. Generate NGC API Key
2. Login with the NGC API as password
```
Username: $oauthtoken
Password:
```
Docker Compose:
1. Install Docker Compose
2. Verify using
```
docker compose version
```

Building the Docker Image

Clone the repository and navigate to the docker directory:

git clone https://github.com/abmoRobotics/RLRoverLab
cd RLRoverLab

Download terrain assets:

pip3 install gdown
python3 download_usd.py

Build and start the Docker container:

cd docker
./run.sh
docker exec -it rover-lab-base bash

Verify the installed versions inside the container:

cd /workspace/rlroverlab
python tools/verify_stack.py

Usage

Training an Agent

To train an agent headlessly, use the following command inside the Docker container:

cd /workspace/rlroverlab
python examples/02_training/train.py --task="AAURoverEnv-v0" --num_envs=256 --viz none

Evaluating a Pre-trained Policy

To evaluate a pre-trained policy, use the following command inside the Docker container:

cd /workspace/rlroverlab
python examples/03_inference/eval.py --task="AAURoverEnv-v0" --num_envs=32 --viz none

Running with the Kit viewer

Pass --viz kit from a machine with a working local display and X11 access:

cd /workspace/rlroverlab
python examples/01_demos/01_zero_agent.py --task="AAURoverEnvSimple-v0" --num_envs=1 --viz kit

Isaac Lab 3.0 deprecates --headless; use --viz none to force headless execution. Camera and RGB-D tasks enable their required camera extensions automatically.

The Docker setup bind-mounts the repository to /workspace/rlroverlab/, so any changes you make to the code on your host machine are immediately reflected inside the container. This makes it ideal for development:

Edit code on your host machine using your preferred editor
Run/test inside the container
No need to rebuild the container for code changes

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