Enhancing context-aware human motion prediction for efficient robot handovers

Abstract

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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)