Install & Download

This guide walks through the standard native RoboDojo setup path: check the system requirements, install the codebase and dependencies, download assets, and update local asset paths.

1. System requirements

Supported platforms

RoboDojo is built on Isaac Sim 5.1 and Isaac Lab. We recommend Linux (Ubuntu 22.04 x64) with an NVIDIA GPU.

Most platform constraints come from Isaac Sim and Isaac Lab, so check their official requirements if you are using a different driver, CUDA version, or workstation setup.

Hardware and software

Item	Requirement
OS	Ubuntu 22.04 (Linux x64) recommended
RAM	32 GB or more
GPU VRAM	16 GB or more (extra VRAM may be needed for rendering-heavy workflows)
NVIDIA driver	570 or 580 recommended on Linux
CUDA	12.8 recommended (installed with PyTorch during setup)

For the latest Isaac Sim hardware notes, see the official system requirements.

2. Install Vulkan

Vulkan is required for Isaac Sim rendering. Skip this step if vulkaninfo already reports a working Vulkan installation.

sudo apt install libvulkan1 mesa-vulkan-drivers vulkan-tools
vulkaninfo | head

3. Clone & install RoboDojo natively

3.1 Clone the repository

git clone https://github.com/RoboDojo-Branchmark/RoboDojo.git

3.2 Run the install script

The native install path creates the RoboDojo conda environment and installs Isaac Sim 5.1, Isaac Lab, CuRobo, and the remaining Python dependencies:

bash scripts/install.sh -i

When finished, activate the environment:

conda activate RoboDojo

3.3 Resume from a failed step

If installation stops midway, resume from a named step instead of restarting from scratch:

bash scripts/install.sh --from STEP_NAME

Available step names:

Step name	Resumes from
system	System package setup
conda	Conda environment creation
base_deps	Base Python dependencies
submodules	Git submodule initialization
isaacsim	Isaac Sim installation
isaaclab	Isaac Lab installation
curobo	CuRobo installation

Example - retry after an Isaac Sim failure:

bash scripts/install.sh --from isaacsim

3.4 Verify optional tools

ffmpeg is required for some data-processing workflows. On Ubuntu, the install script tries to install it automatically; you can verify it manually with:

ffmpeg -version

If missing, install from ffmpeg.org or via sudo apt install ffmpeg.

4. Download assets

Simulation assets, including objects, textures, and embodiments, are hosted on ModelScope. Git LFS is required before downloading them.

# Install git-lfs once (Ubuntu)
sudo apt install git-lfs
git lfs install

# Download assets into ./Assets
bash scripts/init_assets.sh

After download, the assets directory should look like:

Assets/
├── Background/
├── Eval_Layout/
│   └── ...
├── Material/
└── ...

5. Update embodiment paths

After the assets are in place, point robot configs to the local asset directory:

python utils/update_embodiment_config_path.py

Run this command again if you move the repository or re-download assets to a different location.

6. Optional: Install with Docker

Docker is useful when you want a reproducible simulator/evaluation container. The RoboDojo image contains Isaac Sim 5.1, Isaac Lab, CuRobo, the RoboDojo Python stack, and the lightweight XPolicyLab client used by the eval client.

The image does not include Assets/, policy dependencies, or checkpoints. Download assets on the host, mount them into the container, and run your policy server outside the container over WebSocket.

6.1 Docker prerequisites

Need	Details
NVIDIA GPU + driver	Driver 570 or newer is recommended for CUDA 12.8. Check with nvidia-smi.
OS	Linux, tested on Ubuntu 22.04.
Disk space	The image is large because it includes Isaac Sim and cached extensions. Keep at least 300 GB free for the image and build cache.
Assets	A host-side Assets/ directory downloaded with bash scripts/init_assets.sh. Assets are mounted at runtime, not baked into the image.

Prepare host-side assets before running container evaluation:

bash scripts/init_assets.sh
python utils/update_embodiment_config_path.py

6.2 Install Docker and NVIDIA Container Toolkit

If Docker and the NVIDIA Container Toolkit already work with --gpus all, skip to the image build step.

sudo bash docker/install_docker_nvidia.sh
newgrp docker

Verify that Docker can see the GPU:

docker run --rm --gpus all nvidia/cuda:12.8.1-cudnn-devel-ubuntu22.04 nvidia-smi

If you are on a restricted China network, the installer can use TUNA/USTC apt mirrors and Docker Hub registry mirrors. It auto-detects this by default; force it with USE_CN_MIRRORS=1 or disable it with USE_CN_MIRRORS=0.

6.3 Build the image

From the repository root:

docker build -t robodojo:cuda12.8 .

The first build can take a long time because it downloads Isaac Sim extensions and compiles CuRobo kernels. Later builds should reuse Docker layer cache.

6.4 Smoke-test Docker

Run the end-to-end Docker smoke procedure:

bash docker/smoke_docker.sh run

This builds the image if needed, starts the bundled demo_policy server on the host, runs one container evaluation, and checks that a result file is written. The demo policy outputs zero actions, so a success_rate of 0.0 is expected; the smoke test is checking the simulator, rendering, WebSocket connection, and result writing path.

Monitor a running smoke test from another terminal:

bash docker/smoke_docker.sh monitor

The smoke script launches the demo server on the host using the native RoboDojo conda environment. If you do not have the native environment and only want Docker, skip the demo smoke step and connect the container to your own policy server.

6.5 Evaluate an external policy server

Start your policy server outside the container and bind it to 0.0.0.0, not only 127.0.0.1, so the container can reach it. Then run the simulator client in Docker:

docker run --rm -it \
  --gpus all --network host --ipc host \
  -v "$PWD/Assets:/workspace/RoboDojo/Assets:ro" \
  -v "$PWD/Assets:$PWD/Assets:ro" \
  -v "$PWD/eval_result:/workspace/RoboDojo/eval_result" \
  -v "$HOME/.cache/warp:/root/.cache/warp" \
  -v "$HOME/.local/share/ov:/root/.local/share/ov" \
  -v "$HOME/.cache/ov:/root/.cache/ov" \
  robodojo:cuda12.8 \
  bash scripts/robodojo.sh client \
    --task stack_bowls \
    --policy-name <POLICY_NAME> \
    --policy-host 127.0.0.1 --policy-port 9999 \
    --ckpt <CKPT_NAME> --eval-num 1

Required client flags are --task, --policy-name, --policy-host, and --policy-port. Common options include --ckpt, --eval-num, --action-type, --env-cfg, and --env-gpu. Add --dry-run to inspect the resolved simulator command without launching Isaac Sim.

6.6 Required mounts

The two Assets mounts are intentional:

Mount	Why it is needed
-v "$PWD/Assets:/workspace/RoboDojo/Assets:ro"	Gives the container access to robot, object, material, and layout assets.
-v "$PWD/Assets:$PWD/Assets:ro"	Preserves absolute host paths baked into CuRobo configs by utils/update_embodiment_config_path.py.
-v "$PWD/eval_result:/workspace/RoboDojo/eval_result"	Persists result artifacts on the host.
Cache mounts under ~/.cache/warp, ~/.local/share/ov, and ~/.cache/ov	Reuse CuRobo kernels and Isaac Sim extension/shader caches between runs.

The second Assets mount prevents errors such as:

ValueError: /abs/host/.../X5A.urdf is not a file

CuRobo robot configs may contain absolute host paths generated by utils/update_embodiment_config_path.py, so the container must be able to access the same absolute path.

6.7 Networking

Policy server hostnames depend on Docker networking:

Policy server location	Docker mode	--policy-host
Same host as the container	--network host	127.0.0.1
Same host, bridge network	--add-host=host.docker.internal:host-gateway	host.docker.internal
Another machine	default networking	The machine’s IP or hostname

If the client cannot reach the policy server, first check that the server is listening on the expected port and is bound to 0.0.0.0.