# An approach for unsupervised interaction clustering in human–robot co-work using spatiotemporal graph convolutional networks

## TL;DR

This paper introduces a method using graph networks to identify and categorize human-robot interactions in industrial settings, improving flexibility and adaptability in human-centered systems.

## Contribution

A novel approach using spatiotemporal graph convolutional networks for unsupervised clustering of human–robot interaction forms in co-work scenarios.

## Key findings

- The approach identified 10 distinct interaction forms in human–robot co-work scenarios.
- The method reveals more granular interaction patterns than existing taxonomies.
- The results support data-driven analysis for flexible, human-centered systems in Industry 5.0.

## Abstract

In this paper, we present an approach to cluster interaction forms in industrial human–robot co-work using spatiotemporal graph convolutional networks (STGCNs). Humans will increasingly work with robots in the future, whereas previously, humans worked side by side, hand in hand, or alone. The growing frequency of robotic and human–robot co-working applications and the requirement to increase flexibility affect the variety and variability of interactions between humans and robots, which can be observed at production workplaces. In this paper, we investigate the variety and variability of human–robot interactions in industrial co-work scenarios where full automation is impractical. To address the challenges of interaction modeling and clustering, we present an approach that utilizes STGCNs for interaction clustering. Data were collected from 12 realistic human–robot co-work scenarios using a high-accuracy tracking system. The approach identified 10 distinct interaction forms, revealing more granular interaction patterns than established taxonomies. These results support continuous, data-driven analysis of human–robot behavior and contribute to the development of more flexible, human-centered systems that are aligned with Industry 5.0.