Enhancing Context-Aware Human Motion Prediction for Efficient Robot Handovers
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arXiv
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| Hauptverfasser: | , , , |
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| Format: | Preprint |
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2025
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| author | Gómez-Izquierdo, Gerard Laplaza, Javier Sanfeliu, Alberto Garrell, Anaís |
| author_facet | Gómez-Izquierdo, Gerard Laplaza, Javier Sanfeliu, Alberto Garrell, Anaís |
| contents | 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 200x faster inference, and requires only 3% of the parameters compared to existing state-of-the-art HMP models. These advancements establish our framework as a highly efficient and scalable solution for real-time human-robot interaction. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2503_00576 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | Enhancing Context-Aware Human Motion Prediction for Efficient Robot Handovers Gómez-Izquierdo, Gerard Laplaza, Javier Sanfeliu, Alberto Garrell, Anaís Robotics 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 200x faster inference, and requires only 3% of the parameters compared to existing state-of-the-art HMP models. These advancements establish our framework as a highly efficient and scalable solution for real-time human-robot interaction. |
| title | Enhancing Context-Aware Human Motion Prediction for Efficient Robot Handovers |
| topic | Robotics |
| url | https://arxiv.org/abs/2503.00576 |