https://github.com/mit-acl/revise
https://github.com/mit-acl/revise
Last synced: 10 months ago
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- Host: GitHub
- URL: https://github.com/mit-acl/revise
- Owner: mit-acl
- License: mit
- Created: 2024-11-20T17:46:47.000Z (over 1 year ago)
- Default Branch: main
- Last Pushed: 2024-11-20T19:26:41.000Z (over 1 year ago)
- Last Synced: 2024-11-20T19:39:53.837Z (over 1 year ago)
- Language: Python
- Size: 11.1 MB
- Stars: 0
- Watchers: 3
- Forks: 0
- Open Issues: 0
-
Metadata Files:
- Readme: README.md
- License: LICENSE
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README
# REVISE: Robust Probabilistic Motion Planning in a Gaussian Random Field
## Citation
If you use REVISE, please cite our paper ([pdf](https://arxiv.org/abs/2411.13369))
```bibtex
@misc{rose2024reviserobustprobabilisticmotion,
title={REVISE: Robust Probabilistic Motion Planning in a Gaussian Random Field},
author={Alex Rose and Naman Aggarwal and Christopher Jewison and Jonathan P. How},
year={2024},
eprint={2411.13369},
archivePrefix={arXiv},
primaryClass={cs.RO},
url={https://arxiv.org/abs/2411.13369},
}
```
## General Setup
REVISE has been tested with Python 3.12.7 on MacOS and on Ubuntu 20.04.
Installing dependencies (using a virtual environment is recommended):
* `pip install -r requirements.txt`
Replicating paper results:
* `python run_quadrotor_experiment.py` to regenerate belief roadmaps for the single-query and multi-query experiments
* `python run_monte_carlo_simulation.py` to generate random goals for the multi-query experiment and simulate closed-loop trajectories for both experiments
* `python run_metrics_evaluation.py` to evaluate final state MSE, Wasserstein distance between the planned and actual final state distributions, and plan cost for both experiments
* `python plot_results.py` to regenerate the plots used in the paper
## Documentation and Website
Documentation is auto-generated based on the source code and hosted by Read the Docs. Our project page is online at https://acl.mit.edu/REVISE/.
## Acknowledgements
This research was supported by the National Science Foundation Graduate Research Fellowship under grant no. 2141064 and by the Draper Scholars program.
## Website License
This work is licensed under a Creative Commons Attribution-ShareAlike 4.0 International License.