2026-10-07T04:59:08Z https://api.figshare.com/v2/oai

oai:figshare.com:article/32640372 2026-10-01T16:31:45Z portal_1172 item_type_8 month_year_10_2026

Deep learning of dyadic interaction visual cues for human-robot collaboration in assembly tasks Samuel Oluwaseum Adebayo (24169224) PUREID: 617136262 Dyadic Interaction deep learning computer vision machine learning task recognition action recognition assembly task QUB-PHEO gaze estimation <p></p><p>This thesis examines the integration of multiple visual cues in dyadic interactions through deep learning to improve interactivity and intuitiveness in human-robot interactions, particularly in assembly tasks. Serving as a preliminary effort, these bodies of work seek to address the critical deficiencies in current dyadic human robot interaction methodologies, which typically depend on inadequate or isolated visual cues, failing to capture the dynamic and subtle human intentions essential for effective human-robot collaboration, especially within the framework of Industry 5.0.With a focus on dyadic interaction, this research establishes a foundational framework for transferring insights from human-to-human and human-to-robot surrogate interactions to facilitate effective human-robot collaborations, with novel contributions such as a gaze estimation model for subtle cue recognition and a multi-view dataset supporting advanced multi-view and intention-aware HRI frameworks.</p> 2026-10-01T16:31:45Z Text Thesis 10.17034/32640372.v1 https://figshare.com/articles/thesis/Deep_learning_of_dyadic_interaction_visual_cues_for_human-robot_collaboration_in_assembly_tasks/32640372 All Rights Reserved