Project Website

We present the Habitat-Matterport 3D Open Vocabulary Object Goal Navigation dataset (HM3D-OVON), a large-scale benchmark that broadens the scope and se- mantic range of prior Object Goal Navigation (ObjectNav) benchmarks. Leveraging the HM3DSem dataset, HM3D-OVON incorporates over 15k annotated instances of household objects across 379 distinct categories, derived from photo-realistic 3D scans of real-world environments. In contrast to earlier ObjectNav datasets, which limit goal objects to a predefined set of 6-21 categories, HM3D-OVON facilitates the training and evaluation of models with an open-set of goals defined through free-form language at test-time. Through this open-vocabulary formulation, HM3D-OVON encourages progress towards learning visuo-semantic navigation behaviors that are capable of searching for any object specified by text in an open- vocabulary manner. Additionally, we systematically evaluate and compare several different types of approaches on HM3D-OVON. We find that HM3D-OVON can be used to train an open-vocabulary ObjectNav agent that achieves both higher performance and is more robust to localization and actuation noise than the state-of-the-art ObjectNav approach. We hope that our benchmark and baseline results will drive interest in developing embodied agents that can navigate real-world spaces to find household objects specified through free-form language, taking a step towards more flexible and human-like semantic visual navigation. Videos available at: naoki.io/ovon.

Create the conda environment and install all of the dependencies. Mamba is recommended for faster installation:

conda_env_name=ovon
mamba create -n $conda_env_name python=3.7 cmake=3.14.0 -y
mamba install -n $conda_env_name \
  habitat-sim=0.2.3 headless pytorch=1.12.1  cudatoolkit=11.3 \
  -c pytorch -c nvidia -c conda-forge -c aihabitat -y

# Install this repo as a package
mamba activate $conda_env_name
pip install -e .

# Install distributed_dagger and frontier_exploration
cd frontier_exploration && pip install -e . && cd ..

# Install habitat-lab
git clone --branch v0.2.3 [email protected]:facebookresearch/habitat-lab.git
cd habitat-lab
pip install -e habitat-lab
pip install -e habitat-baselines

pip install ftfy regex tqdm GPUtil trimesh seaborn timm scikit-learn einops transformers
pip install git+https://github.com/openai/CLIP.git

First, set the following variables during installation (don't need to put in .bashrc):

MATTERPORT_TOKEN_ID=<FILL IN FROM YOUR ACCOUNT INFO IN MATTERPORT>
MATTERPORT_TOKEN_SECRET=<FILL IN FROM YOUR ACCOUNT INFO IN MATTERPORT>
DATA_DIR=</path/to/ovon/data>

# Download HM3D 3D scans (scenes_dataset)
python -m habitat_sim.utils.datasets_download \
  --username $MATTERPORT_TOKEN_ID --password $MATTERPORT_TOKEN_SECRET \
  --uids hm3d_train_v0.2 \
  --data-path $DATA_DIR &&
python -m habitat_sim.utils.datasets_download \
  --username $MATTERPORT_TOKEN_ID --password $MATTERPORT_TOKEN_SECRET \
  --uids hm3d_val_v0.2 \
  --data-path $DATA_DIR

The OVON navigation episodes can be found here: https://huggingface.co/datasets/nyokoyama/hm3d_ovon/ The tar.gz file should be decompressed in data/datasets/ovon/, such that the hm3d directory is located at data/datasets/ovon/hm3d/.

The weights for the DagRL policy can be downloaded from the following link:

• dagrl.pth: https://drive.google.com/drive/folders/1U-tnPYQa81JbYHSlW1nyjiXOK8cE2Ki8?usp=sharing

Run the following to evaluate:

python -m ovon.run \
  --run-type eval \
  --exp-config config/experiments/dagger_objectnav.yaml \
  habitat_baselines.eval_ckpt_path_dir=<path_to_ckpt>

Run the following to train:

python -m ovon.run \
  --run-type train \
  --exp-config config/experiments/dagger_objectnav.yaml