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oai:recercat.cat:2117/4595202026-03-31T10:34:03Zcom_2072_1033col_2072_452950

Enhancing context-aware human motion prediction for efficient robot handovers Gomez Izquierdo, Gerard Laplaza Galindo, Javier Sanfeliu Cortés, Alberto Garrell Zulueta, Anais Institut de Robòtica i Informàtica Industrial Universitat Politècnica de Catalunya. Departament d'Enginyeria de Sistemes, Automàtica i Informàtica Industrial Universitat Politècnica de Catalunya. RAIG - Mobile Robotics and Artificial Intelligence Group Àrees temàtiques de la UPC::Informàtica::Robòtica Learning (artificial intelligence) Mobile robots Service robots Adaptation models Accuracy Computational modeling Human-robot interaction Computer architecture Handover Real-time systems Forecasting Computational complexity Intelligent robots © 2025 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works. Accurate human motion prediction (HMP) is critical for seamless human-robot collaboration, particularly in handover tasks that require real-time adaptability. Despite the high accuracy of state-of-the-art models, their computational complexity limits practical deployment in real-world robotic applications. In this work, we enhance human motion forecasting for handover tasks by leveraging siMLPe [1], a lightweight yet powerful architecture, and introducing key improvements. Our approach, named IntentMotion incorporates intention-aware conditioning, task-specific loss functions, and a novel intention classifier, significantly improving motion prediction accuracy while maintaining efficiency. Experimental results demonstrate that our method reduces body loss error by over 50%, achieves 200× faster inference, and requires only 3% of the parameters compared to existing state-of-the-art HMP models in robotics. These advancements establish our framework as a highly efficient and scalable solution for real-time human-robot interaction. This work was partially supported by JST Moonshot R, D grant number JPMJMS2011 and LENA (PID2022-142039NA-I00), funded by MCIN/AEI/10.13039/501100011033. Peer Reviewed Postprint (author's final draft) 2025 Conference report Gomez, G. [et al.]. Enhancing context-aware human motion prediction for efficient robot handovers. A: IEEE/RSJ International Conference on Intelligent Robots and Systems. «IROS 2025 IEEE/RSJ International Conference on Intelligent Robots and Systems, 19-25.10.2025 Hangzhou, XIna». 2025, p. 16917-16922. DOI 10.1109/IROS60139.2025.11246683 . https://hdl.handle.net/2117/459520 10.1109/IROS60139.2025.11246683 eng https://ieeexplore.ieee.org/document/11246683 info:eu-repo/grantAgreement/AEI/Plan Estatal de Investigación Científica y Técnica y de Innovación 2021-2023/PID2022-142039NA-I00/ES/APRENDIZAJE CONTINUO PARA LA NAVEGACION DE ROBOTS CON INTERACCIONES HUMANAS/ Open Access 6 p. application/pdf