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https://github.com/RobotLabLTH/skiros2
A skill-based platform for ROS v.2
https://github.com/RobotLabLTH/skiros2
behavior-trees robotics ros
Last synced: about 1 month ago
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A skill-based platform for ROS v.2
- Host: GitHub
- URL: https://github.com/RobotLabLTH/skiros2
- Owner: RobotLabLTH
- License: other
- Created: 2017-11-22T11:03:23.000Z (about 7 years ago)
- Default Branch: master
- Last Pushed: 2024-10-18T00:10:49.000Z (2 months ago)
- Last Synced: 2024-10-20T07:39:09.221Z (2 months ago)
- Topics: behavior-trees, robotics, ros
- Language: Python
- Homepage:
- Size: 3.32 MB
- Stars: 155
- Watchers: 10
- Forks: 20
- Open Issues: 45
-
Metadata Files:
- Readme: README.md
- License: LICENSE.md
Awesome Lists containing this project
- awesome-ros-deliberation - SkiROS2 - Skill-based platform with behavior trees, PDDL task-planning and knowledge integration. (Packages)
README
# SkiROS2: Skill-based robot control platform for ROS
## Overview
SkiROS2 is a platform to create complex robot behaviors by composing _skills_ - modular software blocks - into [behavior trees](https://en.wikipedia.org/wiki/Behavior_tree_(artificial_intelligence,_robotics_and_control)).
Robots coordinated with SkiROS can be used in partially structured environments, where the robot has a good initial understanding of the environment, but it is also expected to find discrepancies, fail using initial plans and react accordingly.
SkiROS offers the following features:
* A **framework** to organize the **robot behaviors** within **modular skill libraries**
* Scalable skill model with **pre-, hold- and post-conditions**
* A **reactive execution engine** based on **Behavior trees**
* A **world model** as a **semantic database** to manage environmental knowledge
* **Reasoning capabilities** and **automatic inference** of skill parameters
* An integration point for **PDDL task planning**
* **Automatic generation of planning domains** based on the skills and entities in the world model
* **ROS**, **RViz** and **tf integration**
* **Python APIs** for skill handling, the world model and task planning
## Getting Started
* **A full introduction and the tutorials are located in the [wiki](https://github.com/RVMI/skiros2/wiki)**
* Watch a video from the [video section below](#Videos)
* Executable skill examples are in the [skiros2_examples repository](https://github.com/RVMI/skiros2_examples)
* The [SkiROS2 paper](https://arxiv.org/abs/2306.17030) provides an overview and background information
* The [skiros2_template_lib](https://github.com/RVMI/skiros2_template_lib) provides a skeleton for new skill library
* Installation instructions are below
## Videos
We have video introductions to the platform with varying lengths. Feel free to choose depending on your time budget. The longer ones always include the content of the short ones.
| 1min Pitch | 5min Short Introduction | 20min ROSCon 2023 |
|---|---|---|
| 
| 
| 
|
| [1min URL](https://www.youtube.com/watch?v=0ejGWLx94a8) | [5min URL](https://www.youtube.com/watch?v=jy-LlNn3e58) | [ROSCon Talk URL](https://vimeo.com/879001825/2a0e9d5412) |
## Installation
### Compatibility
SkiROS is compatible with Ubuntu 18.04/ROS Melodic and Ubuntu 20.04/ROS Noetic, Python 2 and 3. We are also working on a ROS 2 port for Humble.
### Installation Instructions
To use SkiROS you must have [ROS](https://wiki.ros.org/ROS/Installation) installed on your machine.
You also need [pip](https://pip.pypa.io/en/stable/installing/) to install python dependencies.
Clone this repository into your catkin workspace src directory:
```shell
mkdir -p catkin_ws/src && cd catkin_ws/src
git clone https://github.com/RVMI/skiros2
# Clone the base skill set into the skiros2 directory in your catkin workspace.
git clone https://github.com/RVMI/skiros2_std_lib
# Optionally, you can clone the skiros2_examples repositories here as well:
git clone https://github.com/RVMI/skiros2_examples
```
Install dependencies defined in each `package.xml` using [rosdep](http://wiki.ros.org/rosdep) and the python dependencies:
```shell
rosdep install --from-paths . --ignore-src --rosdistro=$ROS_DISTRO -y
# Install Python dependencies
pip install -r requirements.txt --user
```
Build the workspace with catkin
```shell
cd ~/catkin_ws
catkin_make # or 'catkin build'
source ./devel/setup.bash
```
Launch SkiROS2
```shell
roslaunch skiros2 skiros.launch
# Or try one of the examples like
roslaunch skiros2_examples turtlesim_example.launch
```
### Task Planning
Optionally, if you want to use the task planning skill provided in the standard library, install the fast downward planner with the following script:
```shell
cd skiros2/scripts
./install_fd_task_planner.sh
```
When asked for the install folder, you can insert a location of your preference or just leave the default.
### Creating own skills
The [skiros2_template_lib](https://github.com/RVMI/skiros2_template_lib) provides the necessary structure to create a new library from scratch. It also has an example launch file to start the system.
## Build the Doxygen documentation
To generate the doxygen documentation, first install Doxygen and doxypypy for better compatibility with python doc-strings:
```shell
sudo apt-get install -y doxygen
pip install doxypypy --user
```
Then, in the top level directory, generate the documentation and view it with a browser, e.g. Firefox:
```shell
doxygen Doxyfile
firefox skiros2_doc/html/index.html
```
## Citation
The SkiROS2 paper is [available on IEEE Xplore here](https://ieeexplore.ieee.org/abstract/document/10342216) ([arXiv preprint](https://arxiv.org/abs/2306.17030)). If you are using it in your work, we would be pleased if you would cite it:
```bibtex
@inproceedings{mayr2023skiros2,
title={SkiROS2: A Skill-Based Robot Control Platform for ROS},
author={Mayr, Matthias and Rovida, Francesco and Krueger, Volker},
booktitle={2023 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS)},
pages={6273--6280},
year={2023},
organization={IEEE}
}
```
## Acknowledgements
This platform has been developed in the RVMI lab at Aalborg University.
During its creation it has received funding from the European Union’s Horizon 2020 research and innovation programme under grant agreement no. 723658, [Scalable4.0](https://web.archive.org/web/20210618215726/https://www.scalable40.eu/).
This work was partially supported by the [Wallenberg AI, Autonomous Systems and Software Program (WASP)](https://wasp-sweden.org) funded by Knut and Alice Wallenberg Foundation