Note that this repository uses python3. After cloning this repository, cd into it and execute
mkdir -p data/saved_models
Install the packages gym (0.14.0), stable_baselines (2.7.0), numpy (1.16.2), scipy (1.3.1), pybullet (2.5.6), pyyaml (5.1.2) and opencv (4.1.1). Note that the minimalistic installation with pip might be sufficient (opencv-python==4.1.1.26), but is not guaranteed.
To train an agent for 90M steps with 16 worker in parallel stored to the folder test_training, use
python3 train.py -s 90e6 -v v0 -p policies.aslaug_policy_v0.AslaugPolicy -f test_training -n 16
To run a trained agent from folder test_training at episode 60.5M, use
python3 run.py -v v0 -f test_training:60.5M
Every time a training session is started, the Hyperparameters for the training and the environment are loaded from the file params.yaml. Therefore, it is recommended to parametrize new features added to the environments to allow for maximum flexibility and fast adjustment of parameters.
In order to eliminate the need of a functioning ROS installation, we created a script which converts a xacro file to URDF without ROS package references but relative paths. Note that for the conversion, ROS must be installed and the workspace in which the robot description is located must be sourced. Also, note that ROS uses python2, so this file should be executed with python2 as well.
In order to convert a xacro file to a reference-less URDF with all dependencies copied to a subfolder in the urdf folder, use
python xacro_to_pybullet_urdf.py '/path/to/xacro/file'
This will create a new folder in urdf with the same name as the xacro file, filled with a urdf file which describes the robot and a subfolder meshes with all required files (.stl, .dae etc.).
Please cite the following paper when using this repository for your paper:
@article{kindle2020rlwbc,
title={Whole-Body Control of a Mobile Manipulator using End-to-End Reinforcement Learning},
author={Kindle, Julien and Furrer, Fadri and Novkovic, Tonci and Chung, Jen Jen and Siegwart, Roland and Nieto, Juan},
journal={arXiv preprint arXiv:2003.02637},
year={2020}
}