jackfodtik/context_aware_navigation

[CoRL 2023] Context-Aware Deep Reinforcement Learning for Autonomous Robotic Navigation in Unknown Area - - Public code and model

Context_Aware_Navigation

Public code and datasets for Context-Aware Deep Reinforcement Learning for Autonomous Robotic Navigation in Unknown Area, which has been accepted for presentation at the 7th Conference on Robot Learning (CoRL 2023).

we propose a learning-based framework for autonomous navigation in unknown areas, which employs a context-aware policy network to achieve efficient decision-making (i.e., maximize the likelihood of finding the shortest route towards the target destination). Our agent learns a reactive policy over the next waypoint to travel to, in a constantly expanding graph over the agent’s partial map of the environment. We rely on an attention-based neural network to allow the agent to reason about its entire belief at multiple spatial scales, and form a context embedding, which it then uses to sequence local movement decisions informed by long-term objectives.

Demos

Run

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 the environment and collect episode experience.
• model.py - Define attention-based network.
• env.py - Autonomous navigation environments.
• graph_generator.py - Generate and update the partial robot belief.
• node.py - Initialize and update nodes in the partial robot belief.
• sensor.py - Simulate the sensor model of Lidar.
• /model - Trained model.
• /DungeonMaps - Training environments.

Main 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 test parameters in test_parameters.py.
2. Run python test_driver.py

Citation

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

@inproceedings{liang2023context,
  title={Context-Aware Deep Reinforcement Learning for Autonomous Robotic Navigation in Unknown Area},
  author={Liang, Jingsong and Wang, Zhichen and Cao, Yuhong and Chiun, Jimmy and Zhang, Mengqi and Sartoretti, Guillaume Adrien},
  booktitle={Conference on Robot Learning},
  pages={1425--1436},
  year={2023},
  organization={PMLR}
}

Authors

Jingsong Liang, Zhichen Wang, Yuhong Cao, Jimmy Chiun, Mengqi Zhang, Guillaume Sartoretti