ARiADNE

Public code and model of ARiADNE: A Reinforcement learning approach using Attention-based Deep Networks for Exploration, which is accepted for the oral presentation at ICRA 2023.

Run

Dependencies

• python == 3.10.8
• pytorch == 1.12.0
• ray == 2.1.0
• scikit-image == 0.19.3
• scikit-learn == 1.2.0
• scipy == 1.9.3
• matplotlib == 3.6.2
• tensorboard == 2.11.0

Training

1. Set training parameters in parameters.py.
2. Run python driver.py

Evaluation

1. Set parameters in test_parameters.py.
2. Run test_driver.py

Files

• parameters.py Training parameters.
• driver.py Driver of training program, maintain & update the global network.
• runner.py Wrapper of the local network.
• worker.py Interact with environment and collect episode experience.
• model.py Define attention-based network.
• env.py Autonomous exploration environment.
• graph_generator.py Generate and update the collision-free graph.
• node.py Initialize and update nodes in the coliision-free graph.
• sensor.py Simulate the sensor model of Lidar.
• /model Trained model.
• /DungeonMaps Maps of training environments provided by Chen et al..

Demo of ARiADNE

Cite

If you find our work helpful or enlightening, feel free to cite our paper:

@INPROCEEDINGS{cao2023ariadne,
  author={Cao, Yuhong and Hou, Tianxiang and Wang, Yizhuo and Yi, Xian and Sartoretti, Guillaume},
  booktitle={2023 IEEE International Conference on Robotics and Automation (ICRA)}, 
  title={ARiADNE: A Reinforcement learning approach using Attention-based Deep Networks for Exploration}, 
  year={2023},
  volume={},
  number={},
  pages={10219-10225},
  doi={10.1109/ICRA48891.2023.10160565}}

Authors

Yuhong Cao
Tianxiang Hou
Yizhuo Wang
Xian Yi
Guillaume Sartoretti