multi-task-pybullet-drones
Science Score: 77.0%
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✓CITATION.cff file
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Found 1 DOI reference(s) in README -
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Links to: arxiv.org -
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3 of 13 committers (23.1%) from academic institutions -
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Low similarity (13.1%) to scientific vocabulary
Repository
Basic Info
- Host: GitHub
- Owner: trangml
- License: mit
- Language: Python
- Default Branch: master
- Size: 178 MB
Statistics
- Stars: 5
- Watchers: 1
- Forks: 1
- Open Issues: 0
- Releases: 0
Metadata Files
README.md
multi-task-pybullet-drones
This repository builds off the work done in gym-pybullet-drones with extensions focused on exploring multi-task learning.
Key changes include a YAML configuration system for rapidly testing environment designs, rewards, and terminations. New environments can be found in the env directory. These use configurations of the reward and terminations that can be found in rewards.
Simple OpenAI Gym environment based on PyBullet for multi-agent reinforcement learning with quadrotors
- The default
DroneModel.CF2Xdynamics are based on Bitcraze's Crazyflie 2.x nano-quadrotor
Requirements and Installation
The repo was written using Python 3.7 with conda on macOS 10.15 and tested with Python 3.8 on macOS 12, Ubuntu 20.04
On macOS and Ubuntu
Major dependencies are gym, pybullet,
stable-baselines3, and rllib
Video recording requires to have ffmpeg installed, on macOS
bash
$ brew install ffmpeg
On Ubuntu
bash
$ sudo apt install ffmpeg
macOS with Apple Silicon (like the M1 Air) can only install grpc with a minimum Python version of 3.9 and these two environment variables set:
bash
$ export GRPC_PYTHON_BUILD_SYSTEM_OPENSSL=1
$ export GRPC_PYTHON_BUILD_SYSTEM_ZLIB=1
The repo is structured as a Gym Environment
and can be installed with pip install --editable
$ conda create -n drones python=3.8 # or 3.9 on Apple Silicon, see the comment on grpc above
$ conda activate drones
$ pip3 install --upgrade pip
$ git clone https://github.com/utiasDSL/gym-pybullet-drones.git
$ cd gym-pybullet-drones/
$ pip3 install -e .
On Ubuntu and with a GPU available, optionally uncomment line 203 of BaseAviary.py to use the eglPlugin
gym-pybullet-drones Citation
If you wish, please cite our work (link) as
@INPROCEEDINGS{panerati2021learning,
title={Learning to Fly---a Gym Environment with PyBullet Physics for Reinforcement Learning of Multi-agent Quadcopter Control},
author={Jacopo Panerati and Hehui Zheng and SiQi Zhou and James Xu and Amanda Prorok and Angela P. Schoellig},
booktitle={2021 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS)},
year={2021},
volume={},
number={},
pages={},
doi={}
}
References
- Nathan Michael, Daniel Mellinger, Quentin Lindsey, Vijay Kumar (2010) The GRASP Multiple Micro UAV Testbed
- Benoit Landry (2014) Planning and Control for Quadrotor Flight through Cluttered Environments
- Julian Forster (2015) System Identification of the Crazyflie 2.0 Nano Quadrocopter
- Carlos Luis and Jeroome Le Ny (2016) Design of a Trajectory Tracking Controller for a Nanoquadcopter
- Shital Shah, Debadeepta Dey, Chris Lovett, and Ashish Kapoor (2017) AirSim: High-Fidelity Visual and Physical Simulation for Autonomous Vehicles
- Eric Liang, Richard Liaw, Philipp Moritz, Robert Nishihara, Roy Fox, Ken Goldberg, Joseph E. Gonzalez, Michael I. Jordan, and Ion Stoica (2018) RLlib: Abstractions for Distributed Reinforcement Learning
- Antonin Raffin, Ashley Hill, Maximilian Ernestus, Adam Gleave, Anssi Kanervisto, and Noah Dormann (2019) Stable Baselines3
- Guanya Shi, Xichen Shi, Michael OConnell, Rose Yu, Kamyar Azizzadenesheli, Animashree Anandkumar, Yisong Yue, and Soon-Jo Chung (2019) Neural Lander: Stable Drone Landing Control Using Learned Dynamics
- Mikayel Samvelyan, Tabish Rashid, Christian Schroeder de Witt, Gregory Farquhar, Nantas Nardelli, Tim G. J. Rudner, Chia-Man Hung, Philip H. S. Torr, Jakob Foerster, and Shimon Whiteson (2019) The StarCraft Multi-Agent Challenge
- C. Karen Liu and Dan Negrut (2020) The Role of Physics-Based Simulators in Robotics
- Yunlong Song, Selim Naji, Elia Kaufmann, Antonio Loquercio, and Davide Scaramuzza (2020) Flightmare: A Flexible Quadrotor Simulator
Bonus GIF for scrolling this far
University of Toronto's Dynamic Systems Lab / Vector Institute / University of Cambridge's Prorok Lab / Mitacs
Owner
- Name: Matthew Trang
- Login: trangml
- Kind: user
- Website: trangml.com
- Repositories: 1
- Profile: https://github.com/trangml
Machine Learning Engineer / Grad Student at VT
Citation (CITATION.cff)
cff-version: 1.2.0
preferred-citation:
type: article
authors:
- family-names: "Panerati"
given-names: "Jacopo"
orcid: "https://orcid.org/0000-0003-2994-5422"
- family-names: "Zheng"
given-names: "Hehui"
orcid: "https://orcid.org/0000-0002-4977-0220"
- family-names: "Zhou"
given-names: "SiQi"
- family-names: "Xu"
given-names: "James"
- family-names: "Prorok"
given-names: "Amanda"
orcid: "https://orcid.org/0000-0001-7313-5983"
- family-names: "Schoellig"
given-names: "Angela P."
orcid: "https://orcid.org/0000-0003-4012-4668"
doi: "10.0000/00000"
journal: "2021 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS)"
month: 1
start: 1 # First page number
end: 8 # Last page number
title: "Learning to Fly---a Gym Environment with PyBullet Physics for Reinforcement Learning of Multi-agent Quadcopter Control"
issue: 1
volume: 1
year: 2021
GitHub Events
Total
- Watch event: 2
- Fork event: 1
Last Year
- Watch event: 2
- Fork event: 1
Committers
Last synced: 10 months ago
Top Committers
| Name | Commits | |
|---|---|---|
| Jacopo Panerati | j****i@r****a | 578 |
| trangml | m****g@v****u | 140 |
| Spencer Teetaert | s****t@g****m | 30 |
| hidal00p | a****k@p****e | 3 |
| Hehui Zheng | 5****g | 3 |
| Hany Hamed | h****r@g****m | 3 |
| Dominik Helfenstein | 4****1 | 2 |
| Umut Kaan Kavaklı | 6****i | 1 |
| SiQi Zhou | s****u@r****a | 1 |
| James Xu | j****u@g****m | 1 |
| Felix Büttner | 3****r | 1 |
| Beomyeol Yu | y****l@g****m | 1 |
| Auki | 5****2 | 1 |
Committer Domains (Top 20 + Academic)
Issues and Pull Requests
Last synced: 10 months ago
All Time
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- Total pull requests: 4
- Average time to close issues: N/A
- Average time to close pull requests: 1 minute
- Total issue authors: 0
- Total pull request authors: 1
- Average comments per issue: 0
- Average comments per pull request: 0.5
- Merged pull requests: 4
- Bot issues: 0
- Bot pull requests: 0
Past Year
- Issues: 0
- Pull requests: 0
- Average time to close issues: N/A
- Average time to close pull requests: N/A
- Issue authors: 0
- Pull request authors: 0
- Average comments per issue: 0
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- Merged pull requests: 0
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- Bot pull requests: 0
Top Authors
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- trangml (2)
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Dependencies
- actions/checkout v3 composite
- actions/setup-python v3 composite
- actions/checkout v3 composite
- actions/setup-python v3 composite
- pypa/gh-action-pypi-publish master composite
- Pillow ^10.0.1
- cycler ^0.10
- gym ^0.21
- matplotlib ^3.5
- numpy ^1.22
- pybullet ^3.2
- python ^3.8
- ray[rllib] 1.9
- scipy ^1.8
- stable-baselines3 ^1.5
- tensorboard ^2.9
- setuptools *
- Pillow *
- cycler *
- gym *
- hydra *
- hydra-core *
- matplotlib *
- numpy *
- omegaconf *
- pybullet *
- ray *
- stable_baselines3 *
- tensorboard *