NaviSTAR

Official code for IROS2023 paper "NaviSTAR: Socially Aware Robot Navigation with Hybrid Spatio-Temporal Graph Transformer and Preference Learning".

For information, please refer to our website.

Abstract

Developing robotic technologies for use in human society requires ensuring the safety of robots' navigation behaviors while adhering to pedestrians' expectations and social norms. However, understanding complex human-robot interactions (HRI) to infer potential cooperation and response among robots and pedestrians for cooperative collision avoidance is challenging. To address these challenges, we propose a novel socially-aware navigation benchmark called NaviSTAR, which utilizes a hybrid Spatio-Temporal grAph tRansformer to understand interactions in human-rich environments fusing crowd multi-modal dynamic features. We leverage an off-policy reinforcement learning algorithm with preference learning to train a policy and a reward function network with supervisor guidance. Additionally, we design a social score function to evaluate the overall performance of social navigation. To compare, we train and test our algorithm with other state-of-the-art methods in both simulator and real-world scenarios independently. Our results show that NaviSTAR outperforms previous methods with outstanding performance.

Requirement

Environment

• OS: Ubuntu 20.04
• CPU: Intel i9-13900K
• GPU: Nvidia Geforce RTX 4090
• Python: 3.8

Dependencies

1. Install Pytorch1.8.1

   pip install torch==1.8.1+cu111 torchvision==0.9.1+cu111 torchaudio==0.8.1 -f https://download.pytorch.org/whl/torch_stable.html

2. Install the dependencies from the requirements.txt:

   pip install -r requirements.txt

3. Install Python-RVO2 library.

Training

1. To train a model with onpolicy algorithm PPO, Please run:

   python train.py

2. To train a model with offpolicy algorithm SAC, Please run:

   python train_sac.py

   Training model will be saved in data/navigation

Evaluating

To evaluate the model performance, please run:

python test.py

or

python test_sac.py

Render

Please run:

python render.py

or

python render_sac.py

.giffile will be saved in gif

Citation

If you find this repository useful, please cite our paper:

@inproceedings{wang2023navistar,
  title={Navistar: Socially aware robot navigation with hybrid spatio-temporal graph transformer and preference learning},
  author={Wang, Weizheng and Wang, Ruiqi and Mao, Le and Min, Byung-Cheol},
  booktitle={2023 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS)},
  pages={11348--11355},
  year={2023},
  organization={IEEE}
}

Acknowledgement

This code partly bases on DSRNN, SAC. We thank the authors for releasing their code.

Contributors

Le Mao, Weizheng Wang, and Byung-Cheol Min