Publication
Enhancing context-aware human motion prediction for efficient robot handovers
Conference Article
Conference
IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS)
Edition
2025
Pages
16917-16922
Doc link
https://doi.org/10.1109/IROS60139.2025.11246683
File
Abstract
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.
Categories
learning (artificial intelligence), mobile robots, service robots.
Scientific reference
G. Gomez, J. Laplaza, A. Sanfeliu and A. Garrell Zulueta. Enhancing context-aware human motion prediction for efficient robot handovers, 2025 IEEE/RSJ International Conference on Intelligent Robots and Systems, 2025, Hanzghou, China, pp. 16917-16922.
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