cobair 4.0.4

CoBaIR is a Python library for Context Based Intention Recognition

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:

• Concept Paper
• Full Paper for IEEE RO-MAN Conference

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 [e-mail]

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 Auer & Arunima Gopikrishnan

Citation

If you use CoBaIR please consider citating it.

@inproceedings{lubitz2023cobair,
  title={Cobair: A python library for context-based intention recognition in human-robot-interaction},
  author={Lubitz, Adrian and Gutzeit, Lisa and Kirchner, Frank},
  booktitle={2023 32nd IEEE International Conference on Robot and Human Interactive Communication (RO-MAN)},
  pages={2003--2009},
  url={https://ieeexplore.ieee.org/document/10309581/},
  doi={10.1109/RO-MAN57019.2023.10309581},
  year={2023},
  month=aug,
  organization={IEEE}
}

@inproceedings{pub12351,
  author = {Auer, Adrian and Arriaga, Octavio and Hassan, Teena and Hoyer, Nina and Kirchner, Elsa Andrea},
  title = {A Bayesian Approach to Context-based Recognition of Human Intention for Context-Adaptive Robot Assistance in Space Missions},
  booktitle = {Proceedings of SpaceCHI 2.0 - Human-Computer Interaction for Space Exploration - A Workshop at CHI 2022. SpaceCHI 2.0: Human-Computer Interaction for Space Exploration (SpaceCHI 2.0-2022), located at ACM CHI 2022, May 1-1, New Orleans, LA, United States},
  year = {2022},
  month = {5},
  publisher = {ACM}
}

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.