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PoliFormer: Scaling On-Policy RL with Transformers Results in Masterful Navigators

PoliFormer arXiv Conference License Python 3.9+ Code style: black

This repository contains the code and data for the paper "PoliFormer: Scaling On-Policy RL with Transformers Results in Masterful Navigators".

🐍 Setting up the Python environment 🐍

🐳 Docker 🐳 [Recommended]

Please see the README.md in the docker directory for instructions on how to build and run the docker image.

or use the pre-built image from Docker Hub:

docker pull khzeng777/spoc-rl:v2

then:

export CODE_PATH=/path/to/this/repo
export DATA_PATH=/path/to/data
export DOCKER_IMAGE=khzeng777/spoc-rl:v2
docker run \
    --gpus all \
    --device /dev/dri \
    --mount type=bind,source=${CODE_PATH},target=/root/poliformer \
    --mount type=bind,source=${DATA_PATH},target=/root/data \
    --shm-size 50G \
    -it ${DOCKER_IMAGE}:latest

and use the following conda environment:

conda activate spoc

🛠 Local installation 🛠 [Not recommended]

pip install -r requirements.txt
pip install --extra-index-url https://ai2thor-pypi.allenai.org ai2thor==0+966bd7758586e05d18f6181f459c0e90ba318bec
pip install --extra-index-url https://miropsota.github.io/torch_packages_builder detectron2==0.6+864913fpt2.1.2cu121
cd DETIC_PATH && git clone https://github.com/facebookresearch/Detic.git --recurse-submodules && cd Detic && $PIP install -r requirements.txt && mkdir models && wget --no-check-certificate https://dl.fbaipublicfiles.com/detic/Detic_LCOCOI21k_CLIP_SwinB_896b32_4x_ft4x_max-size.pth -O models/Detic_LCOCOI21k_CLIP_SwinB_896b32_4x_ft4x_max-size.pth

📊 Data 📊

📥 Downloading the training data 📥

PoliFormer is trained using fifteen from SPOC. The fifteen type has the agent navigating and fetching one of fifteen possible object types. To download the training data for the fifteen type, run the following command:

python -m scripts.download_training_data --save_dir /your/local/save/dir --types fifteen

for example

python -m scripts.download_training_data --save_dir data --types fifteen

📁 Dataset format 📁

Once you run the above command, you will have a directory structure that looks like this

/your/local/save/dir/<fifteen OR all>_type
    <TASK_TYPE>
        house_id_to_sub_house_id_train.json # This file contains a mapping that's needed for train data loading
        house_id_to_sub_house_id_val.json   # This file contains a mapping that's needed for val data loading
        train
            <HOUSEID>
                hdf5_sensors.hdf5 -- containing all the sensors that are not videos
                    <EPISODE_NUMBER>
                        <SENSOR_NAME>
                raw_navigation_camera__<EPISODE_NUMBER>.mp4
                raw_manipulation_camera__<EPISODE_NUMBER>.mp4
        val
            # As with train

The hdf5_sensors.hdf5 contains the necessary information to train PoliFormer, including the house id, starting pose, and target object type/id.

For more information about the downloaded data, including trajectory videos and recorded sensors, please refer to SPOC documentation.

🏋 Training and Evaluation 🏋

In order to run training and evaluation you'll need:

  1. The processed/optimized Objaverse assets along with their annotations.
  2. The set of ProcTHOR-Objaverse houses you'd like to train/evaluate on.
  3. For evaluation only, a trained model checkpoint.

Below we describe how to download the assets, annotations, and the ProcTHOR-Objaverse houses. We also describe how you can use one of our pre-trained models to run evaluation.

💾 Downloading assets, annotations, and houses 💾

📦 Downloading optimized Objaverse assets and annotations 📦

Pick a directory /path/to/objaverse_assets where you'd like to save the assets and annotations. Then run the following commands:

python -m objathor.dataset.download_annotations --version 2023_07_28 --path /path/to/objaverse_assets
python -m objathor.dataset.download_assets --version 2023_07_28 --path /path/to/objaverse_assets

These will create the directory structure:

/path/to/objaverse_assets
    2023_07_28
        annotations.json.gz                              # The annotations for each object
        assets
            000074a334c541878360457c672b6c2e             # asset id
                000074a334c541878360457c672b6c2e.pkl.gz
                albedo.jpg
                emission.jpg
                normal.jpg
                thor_metadata.json
            ... #  39663 more asset directories

🏠 Downloading ProcTHOR-Objaverse houses 🏠

Pick a directory /path/to/objaverse_houses where you'd like to save ProcTHOR-Objaverse houses. Then run:

python -m scripts.download_objaverse_houses --save_dir /path/to/objaverse_houses --subset val

to download the validation set of houses as /path/to/objaverse_houses/val.jsonl.gz. You can also change val to train to download the training set of houses.

🛣 Setting environment variables 🛣

Next you need to set the following environment variables:

export PYTHONPATH=/path/to/poliformer
export OBJAVERSE_HOUSES_DIR=/path/to/objaverse_houses
export OBJAVERSE_DATA_DIR=/path/to/objaverse_assets
export DETIC_REPO_PATH=/path/to/DETIC_PATH

For training, we recommend to set two more environment variables to avoid timeout issues from AllenAct:

export ALLENACT_DEBUG=True
export ALLENACT_DEBUG_VST_TIMEOUT=2000

🚀 Running training 🚀

python training/online/dinov2_vits_tsfm_rgb_augment_objectnav.py train --num_train_processes NUM_OF_TRAIN_PROCESSES --output_dir PATH_TO_RESULT --dataset_dir PATH_TO_DATASET

for example

python training/online/dinov2_vits_tsfm_rgb_augment_objectnav.py train --num_train_processes 32 --output_dir results --dataset_dir data/fifteen/ObjectNavType

🚀 Running evaluation with a pretrained model 🚀

Download pretrained ckpt:

python scripts/download_trained_ckpt.py --save_dir PATH_TO_SAVE_DIR

for example:

python scripts/download_trained_ckpt.py --save_dir ckpt

Run evaluation using text-nav model:

python training/online/online_eval.py --output_basedir PATH_TO_RESULT --num_workers NUM_WORKERS --ckpt_path ckpt/text_nav/model.ckpt --training_tag text-nav --house_set objaverse --gpu_devices 0 1 2 3 4 5 6 7

Run evaluation using pure box-nav model:

python training/online/online_eval.py --output_basedir PATH_TO_RESULT --num_workers NUM_WORKERS --ckpt_path ckpt/box_nav/model.ckpt --training_tag text-nav --house_set objaverse --gpu_devices 0 1 2 3 4 5 6 7 --input_sensors raw_navigation_camera nav_task_relevant_object_bbox nav_accurate_object_bbox --ignore_text_goal

Run evaluation using text+box-nav model:

python training/online/online_eval.py --output_basedir PATH_TO_RESULT --num_workers NUM_WORKERS --ckpt_path ckpt/text_box_nav/model.ckpt --training_tag text-nav --house_set objaverse --gpu_devices 0 1 2 3 4 5 6 7 --input_sensors raw_navigation_camera nav_task_relevant_object_bbox nav_accurate_object_bbox

📝 Cite us 📝

@article{zeng2024poliformer,        
    author    = {Zeng, Kuo-Hao and Zhang, Zichen and Ehsani, Kiana and Hendrix, Rose and Salvador, Jordi and Herrasti, Alvaro and Girshick, Ross and Kembhavi, Aniruddha and Weihs, Luca},
    title     = {PoliFormer: Scaling On-Policy RL with Transformers Results in Masterful Navigators},
    journal   = {CoRL},
    year      = {2024},
}