Physics-based CartPole and Quadrotor Gym environments (using PyBullet) with symbolic a priori dynamics (using CasADi) for learning-based control, and model-free and model-based reinforcement learning (RL).
These environments include (and evaluate) symbolic safety constraints and implement input, parameter, and dynamics disturbances to test the robustness and generalizability of control approaches. [PDF]
@article{brunke2021safe,
title={Safe Learning in Robotics: From Learning-Based Control to Safe Reinforcement Learning},
author={Lukas Brunke and Melissa Greeff and Adam W. Hall and Zhaocong Yuan and Siqi Zhou and Jacopo Panerati and Angela P. Schoellig},
journal = {Annual Review of Control, Robotics, and Autonomous Systems},
year={2021},
url = {https://arxiv.org/abs/2108.06266}}To reproduce the results in the article, see branch ar.
@article{yuan2021safecontrolgym,
author={Yuan, Zhaocong and Hall, Adam W. and Zhou, Siqi and Brunke, Lukas and Greeff, Melissa and Panerati, Jacopo and Schoellig, Angela P.},
journal={IEEE Robotics and Automation Letters},
title={Safe-Control-Gym: A Unified Benchmark Suite for Safe Learning-Based Control and Reinforcement Learning in Robotics},
year={2022},
volume={7},
number={4},
pages={11142-11149},
doi={10.1109/LRA.2022.3196132}}To reproduce the results in the article, see branch submission.
git clone https://github.com/utiasDSL/safe-control-gym.git
cd safe-control-gymCreate and access a Python 3.10 environment using
conda
conda create -n safe python=3.10
conda activate safeInstall the safe-control-gym repository
python -m pip install --upgrade pip
python -m pip install -e .You may need to separately install gmp, a dependency of pycddlib:
conda install -c anaconda gmpor
sudo apt-get install libgmp-devYou may need to separately install acados for fast MPC implementations.
- To build and install acados, see their installation guide.
- To set up the acados python interface, check out these installtion steps.
Overview of safe-control-gym's API:
Familiarize with APIs and environments with the scripts in examples/
cd ./examples/ # Navigate to the examples folder
python3 pid/pid_experiment.py \
--algo pid \
--task quadrotor \
--overrides \
./pid/config_overrides/quadrotor_3D/quadrotor_3D_tracking.yaml
cd ./examples/ # Navigate to the examples folder
python3 lqr/lqr_experiment.py \
--algo lqr \
--task cartpole \
--overrides \
./lqr/config_overrides/cartpole/cartpole_stabilization.yaml \
./lqr/config_overrides/cartpole/lqr_cartpole_stabilization.yamlcd ./examples/rl/ # Navigate to the RL examples folder
python3 rl_experiment.py \
--algo ppo \
--task quadrotor \
--overrides \
./config_overrides/quadrotor_2D/quadrotor_2D_track.yaml \
./config_overrides/quadrotor_2D/ppo_quadrotor_2D.yaml \
--kv_overrides \
algo_config.training=Falsecd ./examples/ # Navigate to the examples folder
python3 no_controller/verbose_api.py \
--task cartpole \
--overrides no_controller/verbose_api.yaml
We compare the sample efficiency of safe-control-gym with the original OpenAI Cartpole and PyBullet Gym's Inverted Pendulum, as well as gym-pybullet-drones.
We choose the default physic simulation integration step of each project.
We report performance results for open-loop, random action inputs.
Note that the Bullet engine frequency reported for safe-control-gym is typically much finer grained for improved fidelity.
safe-control-gym quadrotor environment is not as light-weight as gym-pybullet-drones but provides the same order of magnitude speed-up and several more safety features/symbolic models.
| Environment | GUI | Control Freq. | PyBullet Freq. | Constraints & Disturbances^ | Speed-Up^^ |
|---|---|---|---|---|---|
| Gym cartpole | True | 50Hz | N/A | No | 1.16x |
| InvPenPyBulletEnv | False | 60Hz | 60Hz | No | 158.29x |
| cartpole | True | 50Hz | 50Hz | No | 0.85x |
| cartpole | False | 50Hz | 1000Hz | No | 24.73x |
| cartpole | False | 50Hz | 1000Hz | Yes | 22.39x |
| gym-pyb-drones | True | 48Hz | 240Hz | No | 2.43x |
| gym-pyb-drones | False | 50Hz | 1000Hz | No | 21.50x |
| quadrotor | True | 60Hz | 240Hz | No | 0.74x |
| quadrotor | False | 50Hz | 1000Hz | No | 9.28x |
| quadrotor | False | 50Hz | 1000Hz | Yes | 7.62x |
^ Whether the environment includes a default set of constraints and disturbances
^^ Speed-up = Elapsed Simulation Time / Elapsed Wall Clock Time; on a 2.30GHz Quad-Core i7-1068NG7 with 32GB 3733MHz LPDDR4X; no GPU
Tests can be run locally by executing:
python3 -m pytest ./tests/ # Run all testsLinting can be run locally with:
pre-commit install # Install the pre-commit hooks
pre-commit autoupdate # Auto-update the version of the hooks
pre-commit run --all # Run the hooks on all files- Brunke, L., Greeff, M., Hall, A. W., Yuan, Z., Zhou, S., Panerati, J., & Schoellig, A. P. (2022). Safe learning in robotics: From learning-based control to safe reinforcement learning. Annual Review of Control, Robotics, and Autonomous Systems, 5, 411-444.
- Yuan, Z., Hall, A. W., Zhou, S., Brunke, L., Greeff, M., Panerati, J., & Schoellig, A. P. (2022). safe-control-gym: A unified benchmark suite for safe learning-based control and reinforcement learning in robotics. IEEE Robotics and Automation Letters, 7(4), 11142-11149.
gym-pybullet-drones: single and multi-quadrotor environmentsstable-baselines3: PyTorch reinforcement learning algorithmsbullet3: multi-physics simulation enginegym: OpenAI reinforcement learning toolkitcasadi: symbolic framework for numeric optimizationsafety-gym: environments for safe exploration in RLrealworldrl_suite: real-world RL challenge frameworkgym-marl-reconnaissance: multi-agent heterogeneous (UAV/UGV) environments
University of Toronto's Dynamic Systems Lab / Vector Institute for Artificial Intelligence
