Stable Worker Intention Recognition Via Transformer and CRF-Ontology Decoding for Human–Robot Collaboration

Abstract

This paper proposes a transformer-based single stream model with CRF–ontology decoding for stable worker intention recognition in human–robot collaboration(HRC). Although existing intention recognition methods achieve high accuracy, they often suffer from temporal prediction instability and logically inconsistent combinations among actions, tools, parts, and intentions. To address these issues, the proposed approach employs a transformer encoder to integrate worker actions and part-related information, thereby capturing the task context and jointly predicting actions, tools, parts, and intentions. For intention prediction, a conditional random field (CRF) is applied to enforce temporal consistency and improve prediction stability. In addition, an ontology-based postprocessing step removes infeasible combinations under a given task intention and reselects predictions that satisfy structural constraints. Experimental results show that the CRF reduces the intention change rate from 7.9% to 3.0%, improving temporal stability, while ontology-based decoding decreases the violation rate from 26.5% to 6.9% by eliminating inconsistent predictions. When combined, the proposed method achieves both a low change rate (3.0%) and a low violation rate (3.7%), demonstrating its effectiveness for reliable intention recognition in HRC.