20.500.12556/DiRROS-25143 Human intention recognition by deep LSTM and transformer networks for real-time human-robot collaboration Collaboration between humans and robots is essential for optimizing the performance of complex tasks in industrial environments, reducing worker strain, and improving safety. This paper presents an integrated human-robot collaboration (HRC) system that leverages advanced intention recognition for real-time task sharing and interaction. By utilizing state-of-the-art human pose estimation combined with deep learning models, we developed a robust framework for detecting and predicting worker intentions. Specifically, we employed LSTM-based and transformer-based neural networks with convolutional and pooling layers to classify human hand trajectories, achieving higher accuracy compared to previous approaches. Additionally, our system integrates dynamic movement primitives (DMPs) for smooth robot motion transitions, collision prevention, and automatic motion onset/cessation detection. We validated the system in a real-world industrial assembly task, demonstrating its effectiveness in enhancing the fluency, safety, and efficiency of human-robot collaboration. The proposed method shows promise in improving real-time decision-making in collaborative environments, offering a safer and more intuitive interaction between humans and robots. human-robot collaboration deep neural networks LSTM transformer intention recognition nevronske mreže sodelovanje med ljudmi in roboti true false true Frontiers Švica Angleški jezik Slovenski jezik © 2025 Mavsar, Simonič and Ude. Neznano 2026-01-12 14:12:34 2026-01-12 14:12:34 2026-01-13 03:45:17 0000-00-00 00:00:00 2025 0 0 Nasl. z nasl. zaslona; Opis vira z dne 6. 1. 2026; str. 1-15 Vol. 12 19 Dec. 2025 0000-00-00 Zaloznikova Objavljeno NiDoloceno 2025-09-19 2025-11-26 2025-12-19 004.5 2296-9144 doi.org/10.3389/frobt.2025.1708987 263645187 https://www.frontiersin.org/journals/robotics-and-ai/articles/10.3389/frobt.2025.1708987/full 1 5faf1f22-efb8-11f0-94a7-001a4af901a5 https://dirros.openscience.si/Dokument.php?lang=slv&id=37880 RAZ_Mavsar_Matija_2025.pdf RAZ_Mavsar_Matija_2025.pdf 1 CAEAFF9578940192BCCB617D386F9090 49853e91a868584a9b8b62f0deef892e046137bde23abc2bc0bca5b4faef28f4 ed70e9d4-efb8-11f0-94a7-001a4af901a5 https://dirros.openscience.si/Dokument.php?lang=slv&id=37883 Institut Jožef Stefan 0 0 0