Multi-Scale Incremental Modeling for Enhanced Human Motion Prediction in Human-Robot Collaboration

• URL: http://arxiv.org/abs/2412.11632v1
• Date: Mon, 16 Dec 2024 10:20:46 GMT
• Title: Multi-Scale Incremental Modeling for Enhanced Human Motion Prediction in Human-Robot Collaboration
• Authors: Juncheng Zou,
• Abstract summary: This paper presents a novel framework that explicitly encodes incremental models across multiple-temporal scales. Experiments on four datasets demonstrate substantial improvements in continuity, biomechanical consistency, and long-term forecast stability. The proposed multi-scale incremental approach provides a powerful technique for advancing human motion prediction capabilities critical for seamless human-robot interaction.
• Score: 0.0
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• Abstract: Accurate human motion prediction is crucial for safe human-robot collaboration but remains challenging due to the complexity of modeling intricate and variable human movements. This paper presents Parallel Multi-scale Incremental Prediction (PMS), a novel framework that explicitly models incremental motion across multiple spatio-temporal scales to capture subtle joint evolutions and global trajectory shifts. PMS encodes these multi-scale increments using parallel sequence branches, enabling iterative refinement of predictions. A multi-stage training procedure with a full-timeline loss integrates temporal context. Extensive experiments on four datasets demonstrate substantial improvements in continuity, biomechanical consistency, and long-term forecast stability by modeling inter-frame increments. PMS achieves state-of-the-art performance, increasing prediction accuracy by 16.3%-64.2% over previous methods. The proposed multi-scale incremental approach provides a powerful technique for advancing human motion prediction capabilities critical for seamless human-robot interaction.

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