Human-Robot handover task intention recognition framework by fusing human digital twin and deep domain adaptation

Abstract

Industry 5.0 is conceived to achieve the expected high level of smart manufacturing and the more proactive and adaptive human-robot collaboration (HRC). In particular, human-robot handover tasks (HRHTs) are a notably crucial aspect of HRC, and resolving how to motivate robots to comprehend human handover intentions constitutes an exigent issue that demands resolution. To address these problems, A framework that integrates hierarchical human digital twin (HHDT) and deep domain adaptation is proposed, where HHDT model is built to digitise the human physical and physiological state and enhance the robot's perception of humans, a feature extractor-based on bidirectional long-short-term memory and spatio-temporal graph convolution network (BiLSTM-ST-GCN) is devised to extract latent spatio-temporal features of HRHTs intentions. Additionally, the framework incorporates a deep domain adaptation layer (DDAL) that facilitates knowledge transfer of HRHTs intentions by accounting for the distribution differences between the source and target operator objects. Extensive experiments demonstrate the superior performance of the proposed framework for HRHTs intention recognition and extraction of domain invariant features.