Simulation Environments Setup#

Our toolchain provides two Python environment solutions to accommodate different usage scenarios with the InternNav-N1 series model:

  • For quick trials and evaluations of the InternNav-N1 model, we recommend using the Habitat environment. This option offer allowing you to quickly test and eval the InternVLA-N1 models with minimal configuration.

  • If you require high-fidelity rendering, training capabilities, and physical property evaluations within the environment, we suggest using the Isaac Sim environment. This solution provides enhanced graphical rendering and more accurate physics simulations for comprehensive testing.

Choose the environment that best fits your specific needs to optimize your experience with the InternNav-N1 model. Note that both environments support the training of the system1 model NavDP.

Install with Isaac Sim Environment#

Install from Docker Image#

To help you get started quickly, we’ve prepared a Docker image pre-configured with Isaac Sim 4.5, InternUtopia and models. A detailed guideline can be found at challenge page.

You can pull the image (~17GB) and run evaluations in the container using the following command:

docker pull crpi-mdum1jboc8276vb5.cn-beijing.personal.cr.aliyuncs.com/iros-challenge/internnav:v1.2

Run the container by:

xhost +local:root # Allow the container to access the display

cd PATH/TO/INTERNNAV/  # where the latest code pulled

docker run --name internnav -it --rm --gpus all --network host \
  -e "ACCEPT_EULA=Y" \
  -e "PRIVACY_CONSENT=Y" \
  -e "DISPLAY=${DISPLAY}" \
  --entrypoint /bin/bash \
  -w /root/InternNav \
  -v /tmp/.X11-unix/:/tmp/.X11-unix \
  -v ${PWD}:/root/InternNav \
  -v ${HOME}/docker/isaac-sim/cache/kit:/isaac-sim/kit/cache:rw \
  -v ${HOME}/docker/isaac-sim/cache/ov:/root/.cache/ov:rw \
  -v ${HOME}/docker/isaac-sim/cache/pip:/root/.cache/pip:rw \
  -v ${HOME}/docker/isaac-sim/cache/glcache:/root/.cache/nvidia/GLCache:rw \
  -v ${HOME}/docker/isaac-sim/cache/computecache:/root/.nv/ComputeCache:rw \
  -v ${HOME}/docker/isaac-sim/logs:/root/.nvidia-omniverse/logs:rw \
  -v ${HOME}/docker/isaac-sim/data:/root/.local/share/ov/data:rw \
  -v ${HOME}/docker/isaac-sim/documents:/root/Documents:rw \
  -v ${PWD}/data/scene_data/mp3d_pe:/isaac-sim/Matterport3D/data/v1/scans:rw \
  crpi-mdum1jboc8276vb5.cn-beijing.personal.cr.aliyuncs.com/iros-challenge/internnav:v1.2

After the container started, you can quickly start the env and install the InternNav:

conda activate internutopia
pip install -e .[isaac,model]

Conda Installation from Scratch#

Prerequisite

  • Ubuntu 20.04, 22.04

  • Python 3.10.16 (3.10.* should be ok)

  • NVIDIA Omniverse Isaac Sim 4.5.0

  • NVIDIA GPU (RTX 2070 or higher)

  • NVIDIA GPU Driver (recommended version 535.216.01+)

  • PyTorch 2.5.1, 2.6.0 (recommended)

  • CUDA 11.8, 12.4 (recommended)

Before proceeding with the installation, ensure that you have Isaac Sim 4.5.0 and Conda installed.

conda create -n <env> python=3.10 libxcb=1.14

# Install InternUtopia through pip.(2.1.1 and 2.2.0 recommended)
conda activate <env>
pip install internutopia

# Configure the conda environment.
python -m internutopia.setup_conda_pypi
conda deactivate && conda activate <env>

For InternUtopia installation, you can find more detailed docs in InternUtopia.

# Install PyTorch based on your CUDA version
pip install torch==2.5.1 torchvision==0.20.1 torchaudio==2.5.1 --index-url https://download.pytorch.org/whl/cu118

# Install other deps
cd Path/to/InternNav/
pip install -e .[isaac]

Install with Habitat Environment#

If you need to train or evaluate models on Habitat without physics simulation, we recommend the following setup and easier environment installation.

Prerequisite#

  • Python 3.9

  • Pytorch 2.6.0

  • CUDA 12.4

  • GPU: NVIDIA A100 or higher (optional for VLA training)

conda create -n <env> python=3.9
conda activate <env>

Install habitat sim and habitat lab:

conda install habitat-sim==0.2.4 withbullet headless -c conda-forge -c aihabitat
git clone --branch v0.2.4 https://github.com/facebookresearch/habitat-lab.git
cd habitat-lab
pip install -e habitat-lab  # install habitat_lab
pip install -e habitat-baselines # install habitat_baselines

Install pytorch and other requirements:

pip install torch==2.6.0 torchvision==0.21.0 torchaudio==2.6.0 --index-url https://download.pytorch.org/whl/cu124
cd Path/to/InternNav/
pip install -e .[habitat,internvla_n1]