NTFields

NTFields: Neural Time Fields for Physics-Informed Robot Motion Planning
Ruiqi Ni, Ahmed H Qureshi

Paper | GitHub | arXiv | Published in ICLR 2023.

Introduction

This repository is the official implementation of "NTFields: Neural Time Fields for Physics-Informed Robot Motion Planning".

Installation

Clone the repository into your local machine:

git clone https://github.com/ruiqini/NTFields --recursive

Install requirements:

conda env create -f NTFields_env.yml
conda activate NTFields

Download datasets and pretrained models, exact and put datasets/ Experiments/ to the repository directory:

Datasets and pretrained model

The repository directory should look like this:

NTFields/
├── datasets/
│   ├── arm/    # 4DOF and 6DOF robot arm, table environment
│   ├── c3d/    # C3D environment
│   ├── gibson/ # Gibson environment
│   └── test/   # box and bunny environment
├── Experiments
│   ├── 4DOF/   # pretrained model for 4DOF arm
│   └── Gib/    # pretrained model for Gibson
•   •   •
•   •   •

Pre-processing

To prepare the Gibson data, run:

python dataprocessing/preprocess.py --config configs/gibson.txt

To prepare the arm data, run:

python dataprocessing/preprocess.py --config configs/arm.txt

Testing

To visualize our path in a Gibson environment, run:

python test/gib_plan.py 

To visualize our path in the 4DOF arm environment, run:

python test/arm_plan.py 

To sample random starts and goals in Gibson environments, run:

python test/sample_sg.py 

To show our statistics result in Gibson environments, run:

python test/gib_stat.py 

Training

To train our model in a Gibson environment, run:

python train/train_gib.py

To train our model in the 4DOF arm environment, run:

python train/train_arm.py 

Citation

Please cite our paper if you find it useful in your research:

@inproceedings{
    ni2023ntfields,
    title={{NTF}ields: Neural Time Fields for Physics-Informed Robot Motion Planning},
    author={Ruiqi Ni and Ahmed H Qureshi},
    booktitle={International Conference on Learning Representations},
    year={2023},
    url={https://openreview.net/forum?id=ApF0dmi1_9K}
}

Acknowledgement

This implementation takes EikoNet and NDF as references. We thank the authors for their excellent work.

License

NTFields is released under the MIT License. See the LICENSE file for more details.