Context_Aware_Navigation
Context_Aware_Navigation copied to clipboard
[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
- Set training parameters in
parameters.py
. - Run
python driver.py
Evaluation
- Set test parameters in
test_parameters.py
. - 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