Transformer Task Planner

Vidhi Jain, Yixin Lin, Eric Undersander, Yonatan Bisk and Akshara Rai, Transformers are Adaptable Task Planners, 6th Conference on Robot Learning (CoRL 2022).

OpenReview | Website

Setup

1. Create conda environment

conda env create -f environment.yml

2. Activate conda environment

• using https://direnv.net/ ...

direnv allow

• or manually activate

conda activate temporal_task_planner

3. Install PyTorch according to your system requirements.

For example: MacOS installation, cpu only

# MacOS Conda binaries are for x86_64 only, for M1 please use wheels
conda install pytorch -c pytorch

4. Init submodules

git submodule update --init --recursive

5. Build custom habitat-sim

If you are installing habitat-sim for the first time, you might need some additional libraries. Follow instructions from habitat_sim: BUILD_FROM_SOURCE.

cd third_party/habitat-sim
./build.sh --bullet --with-cuda --headless  # this might take a while...
cd -

6. Install temporal_task_planner

pip install -e . 

7. [Optional for sweeps on cluster] Install hydra/launcher submitit slurm

pip install hydra-submitit-launcher --upgrade

8. Add habitat-sim to your python path

export PYTHONPATH=:$PWD/third_party/habitat-sim
echo $PYTHONPATH

9. [Optional if using cuda] Set the CUBLAS CONFIG for fixed seed

export CUBLAS_WORKSPACE_CONFIG=:4096:8

--

Data

1. Kitchen assets

Download from scratch and unzip it:

• data_v3.zip
• scenes.zip

2. Pre-generated Demonstrations

Session jsons can be downloaded from wandb. Request access by emailing [email protected].

python scripts/data_download.py

This creates artifacts/ folder containing full-view-single-pref:latest and partial-view-single-pref:latest datasets, where each contains train, val and test jsons.

---

Run

All the files in scripts/ folder can be run as

python scripts/<filename>.py

Scripts 1-3 are dependent on hydra yaml config files.

0. To download data:

python scripts/data_download.py This downloads the latest single preference data for full and partial visibility scenarios.

1. To rollout expert policy:

python scripts/rollout_batch.py You need to provide config parameters like dirpath, session_id_start, session_id_end.

2. To generate session videos:

python scripts/view_batch.py You need to provide config parameters like dirpath, session_id_start, session_id_end.

3. To train the model:

python scripts/learner.py You need to provide config parameters like pick_only, context_history, data_name, data_version