CoBaIR: A Python Library for Context-Based Intention Recognition in Human-Robot-Interaction
In 2023 32nd IEEE International Conference on Robot and Human Interactive Communication (RO-MAN), (RO-MAN-2023), 28.8.-31.8.2023, Busan, IEEE, 2023.
Human-Robot Interaction (HRI) becomes more
and more important in a world where robots integrate fast in
all aspects of our lives but HRI applications depend massively
on the utilized robotic system as well as the deployment
environment and cultural differences. Because of these variable
dependencies it is often not feasible to use a data-driven
approach to train a model for human intent recognition. Expert
systems have been proven to close this gap very efficiently.
Furthermore, it is important to support understandability in
HRI systems to establish trust in the system. To address the
above-mentioned challenges in HRI we present an adaptable
python library in which current state-of-the-art Models for
context recognition can be integrated. For Context-Based Intention
Recognition a two-layer Bayesian Network (BN) is used.
The bayesian approach offers explainability and clarity in
the creation of scenarios and is easily extendable with more
modalities. Additionally, it can be used as an expert system if
no data is available but can as well be fine-tuned when data