Towards a Neuronally Consistent Ontology for Robotic Agents

TL;DR

This paper explores integrating neuronal information processing into a shared ontology for robotic agents by analyzing fMRI data to align robot and human activity representations.

Contribution

It introduces a method to validate and adapt robot ontologies using neuroimaging data, enhancing human-like understanding in robotic systems.

Findings

Identified brain networks correlated with activity event categories.

Found stable neural network patterns linked to specific environmental contexts.

Demonstrated potential for neuroimaging data to inform ontology development.

Abstract

The Collaborative Research Center for Everyday Activity Science & Engineering (CRC EASE) aims to enable robots to perform environmental interaction tasks with close to human capacity. It therefore employs a shared ontology to model the activity of both kinds of agents, empowering robots to learn from human experiences. To properly describe these human experiences, the ontology will strongly benefit from incorporating characteristics of neuronal information processing which are not accessible from a behavioral perspective alone. We, therefore, propose the analysis of human neuroimaging data for evaluation and validation of concepts and events defined in the ontology model underlying most of the CRC projects. In an exploratory analysis, we employed an Independent Component Analysis (ICA) on functional Magnetic Resonance Imaging (fMRI) data from participants who were presented with the same complex video stimuli of activities as robotic and human agents in different environments and contexts. We then correlated the activity patterns of brain networks represented by derived components with timings of annotated event categories as defined by the ontology model. The present results demonstrate a subset of common networks with stable correlations and specificity towards particular event classes and groups, associated with environmental and contextual factors. These neuronal characteristics will open up avenues for adapting the ontology model to be more consistent with human information processing.