CoBaIR is a Python library for Context Based Intention Recognition
Project description
CoBaIR is a python library for Context Based Intention Recognition.
It provides the means to infer an intention from given context.
An intention is a binary value e.g. repair pipe
that can either be present or not. Only one intention can be present at a time.
Context on the otherhand can have multiple discrete instantiations e.g. weather:sunny|cloudy|raining
.
If context values are continuous, discretizer functions can be used to create discrete values.
From the inferred intention in a HRI scenario the robot can perform corresponding actions to help the human with a specific task.
Publications
For a more in-depth explanation consult the following papers:
Install
pip install CoBaIR
You can install the library from your local copy after cloning this repo with pip using pip install .
or install the newest experimental features from the develop
branch with pip install git+https://github.com/dfki-ric/CoBaIR.git@develop
Known Issues
On some Linux Distros there seems to be a problem with a shared library. This Solutions suggests to export LD_PRELOAD=/usr/lib/x86_64-linux-gnu/libstdc++.so.6
which works on Ubuntu 22.04.
Graphical User Interface
To make the configuration of a scenario easier we provide a Graphical User Interface(GUI). The GUI can be started with
python start_configurator.py
if you want to start the GUI with a loaded config use
python start_configurator.py -f config.yml
Tutorial
For a step-by-step guide on how to use CoBaIR, check out our Tutorial.
Documentation
The Documentation can be accessed on https://dfki-ric.github.io/CoBaIR/
Bayesian Approach
In the bayesian approach CoBaIR uses a two-layer Bayesian Net of the following structure.
Config Format
Configs will be saved in yml files. For convenience the is a configurator which can be started with
python start_configurator.py
Bayesian Approach
The configuration file for a two layer bayesian net for context based intention recognition follows the given format:
# List of contexts. Contexts can have different discrete instantiations.
# Number of instantiations must be larger than 1.
# For all discrete instantiations a prior probability must be given(sum for one context must be 1)
contexts:
context 1:
instantiation 1 : float
.
instantiation m_1 : float
context n:
instantiation 1 : float
.
instantiation m_n : float
# List of intentions. Intentions are always binary(either present or not)
# For every intention the context variables and their influence on the intention is given
# [very high, high, medium, low, very low, no] => [5, 4, 3, 2, 1, 0]
intentions:
intention 1:
context 1:
instantiation 1: int # one out of [5, 4, 3, 2, 1, 0]
.
instantiation m_1: int # one out of [5, 4, 3, 2, 1, 0]
context n:
instantiation 1: int # one out of [5, 4, 3, 2, 1, 0]
.
instantiation m_n: int # one out of [5, 4, 3, 2, 1, 0]
intention p:
context 1:
instantiation 1: int # one out of [5, 4, 3, 2, 1, 0]
.
instantiation m_1: int # one out of [5, 4, 3, 2, 1, 0]
context n:
instantiation 1: int # one out of [5, 4, 3, 2, 1, 0]
.
instantiation m_n: int # one out of [5, 4, 3, 2, 1, 0]
# decision_threshold is a float value between 0 and 1 which decides
# when an intention should be considered in inference.
# Probability must be greater than decision_threshold.
decision_threshold: float
How to contribute
If you find any Bugs or want to contribute/suggest a new feature you can create a Merge Request / Pull Request or contact me directly via adrian.lubitz@dfki.de
Run tests
Tests are implemented with pytest. To install test dependencies you need to run
pip install -r requirements/test_requirements.txt
Then you can run
python -m pytest tests/
You can as well see the test report for a specific commit in gitlab under pipeline->Tests
Coverage
If you want to see coverage for the tests you can run
coverage run -m pytest tests/
Use
coverage report
or
coverage html
You can as well see the coverage for a specific job in gitlab under jobs
To show results of the coverage analysis.
Build docu
Documentation is implemented with the material theme for mkdocs.
Dependencies
Install all dependencies for building the docu with
pip install -r requirements/doc_requirements.txt
Build
Build the docu with
mkdocs build
The documentation will be in the site
folder.
Authors
Adrian Lubitz & Arunima Gopikrishnan
Funding
CoBaIR is currently developed in the Robotics Group of the University of Bremen, together with the Robotics Innovation Center of the German Research Center for Artificial Intelligence (DFKI) in Bremen. CoBaIR has been funded by the German Federal Ministry for Economic Affairs and Energy and the German Aerospace Center (DLR). CoBaIR been used and/or developed in the KiMMI-SF project.
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