This repository is a fork of gym-pybullet-drones and implements a reinforcement learning based control policy inspired by Penicka et al. [1].

For documenation and a summary of the results, see our 4-pages whitepaper.

• The drone can follow arbitrary trajectories.
• It is given two next target waypoints as observation. If the two target waypoints are close, it will reach the target slower.
• The learned policy corresponds to the obtained result after slow-phase training in Penicka et al. [1].

• Reward implementation of RL policy proposed by Penicka et al. [1].
• Attitude control action type. In gym-pybullet-drones, only motor-level control using PWM signals is implemented. This repository extends the original implementation and adds a wrapper for sending attitude commands (thrust and bodyrates).
• Random trajectory generation using polynomial minimum snap trajectory generation using large_scale_traj_optimizer [2] for training and test set generation. Implementation in trajectories subfolder.
• Scripts for bechmarking the policy by computing basic benchmarks such as mean and max deviation from the target trajectory and time until completion.

Tested on ArchLinux and Ubuntu. Note that Eigen must be installed on the system. On linux, install via your package manager. E.g. on Ubuntu:

$ sudo apt-get install libeigen3-dev

It is strongly recommended to use a python virtual environment such as conda or pyvenv.

1. Initialise repository. Repository must be pulled recursively

$ git clone [email protected]:danielbinschmid/RL-pybullets-cf.git
$ git submodule --init --recursive

2. Initialise virtual environment. Tested with python version 3.10.13. E.g.:

$ pyenv install 3.10.13
$ pyenv local 3.10.13
$ python3 -m venv ./venv
$ source ./venv/bin/activate
$ pip3 install --upgrade pip

3. Install dependencies and build

$ pip3 install -e . # if needed, `sudo apt install build-essential` to install `gcc` and build `pybullet`

Scripts for training, testing and visualization are provided.

To train the RL policy from scratch with our implementation, run

$ cd runnables
$ ./train_rl.sh

It will produce a folder with the weights. Later, this weights folder can be passed to the visualization and testing scripts.

To run our small benchmark suite, run

$ cd runnables
$ ./test_rl.sh
$ ./test_pid.sh

Out of the box, it will use our pre-trained weights. Each bash script produces a .json file with the benchmarks.

To just visualize the control policy, run

$ cd runnables
$ ./vis_rl.sh

Out of the box, it will use our pre-trained weights and randomly generated trajectories.

To generate a test set with random tracks, run

$ cd runnables/utils
$ python gen_eval_tracks.py

To generate the plots used in our whitepaper, run

$ cd runnables
$ ./generate_plots.sh

• Autoformatting with black.

Run all tests from the top folder with

pytest tests/

• Mismatching CMakeCache.txt in trajectories/trajectories_generation. Solution: Remove CMakeCache.txt in build folder of trajectories/trajectories_generation.

• [1]: Penicka, Robert, et al. Learning minimum-time flight in cluttered environments. IEEE Robotics and Automation Letters 7.3 (2022): 7209-7216.
• [2]: Burke, Declan, Airlie Chapman, and Iman Shames. Generating minimum-snap quadrotor trajectories really fast. 2020 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS). IEEE, 2020. github