STAMP - Spatio-Temporal Attention network for Monitoring Persistently

This repository hosts the code for paper Spatio-Temporal Attention Network for Persistent Monitoring of Multiple Mobile Targets, accepted for presentation at IROS 2023.

Run

Requirements

python >= 3.9
pytorch >= 1.11
ray >= 2.0
ortools
scikit-image
scikit-learn
scipy
imageio
tensorboard

Training

1. Set appropriate parameters in arguments.py -> Arguments.
2. Run python driver.py.

Evaluation

1. Set appropriate parameters in arguments.py -> ArgumentsEval.
2. Run python /evals/eval_driver.py.

Files

• arguments.py: Training and evaluation arguments.
• driver.py: Driver of training program, maintain and update the global network.
• runner.py: Wrapper of the local network.
• worker.py: Interact with environment and collect episode experience.
• network.py: Spatio-temporal network architecture.
• env.py: Persistent monitoring environment.
• gaussian_process.py: Gaussian processes (wrapper) for belief representation.
• /evals/*: Evaluation files.
• /utils/*: Utility files for graph, target motion, and TSP.
• /model/*: Trained model.

Demo

Watch AirSim Video

Cite

@inproceedings{wang2023spatio,
  title={Spatio-Temporal Attention Network for Persistent Monitoring of Multiple Mobile Targets},
  author={Wang, Yizhuo and Wang, Yutong and Cao, Yuhong and Sartoretti, Guillaume},
  booktitle={2023 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS)},
  year={2023}
}

Authors: Yizhuo Wang, Yutong Wang, Yuhong Cao, Guillaume Sartoretti