CoBaIR: A Python Library for Context-Based Intention Recognition in Human-Robot-Interaction

TL;DR

CoBaIR is a flexible Python library that employs a Bayesian Network for context-based intention recognition in human-robot interaction, emphasizing explainability, adaptability, and ease of integration of multiple modalities.

Contribution

The paper introduces CoBaIR, a novel Python library that uses a Bayesian Network for context recognition, combining explainability with adaptability for HRI applications.

Findings

Supports integration of multiple modalities.

Offers explainability through Bayesian approach.

Can function without data or be fine-tuned with data.

Abstract

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 becomes available.