通过多尺度增量建模，提升人机协作中动作预测的准确性和稳定性。
Multi-Scale Incremental Modeling for Enhanced Human Motion Prediction in Human-Robot Collaboration
- 并行多尺度增量分支捕捉细微关节变化与整体轨迹移动。
- 在四个数据集上预测精度提升16.3%至64.2%，长期预测更稳定。
- 适合需要高精度动作预判的机器人协作场景使用。
精准的人体动作预测对安全的人机协作至关重要，但复杂多变的人体运动建模仍具挑战。本文提出并行多尺度增量预测（PMS）框架，显式建模跨多时空尺度的增量运动，以捕捉细微关节演化与全局轨迹偏移。PMS采用并行序列分支编码多尺度增量，实现预测的迭代优化；结合全时间跨度损失的多阶段训练策略，融入时序上下文信息。在四个数据集上的大量实验表明，该方法显著提升了连续性、生物力学一致性及长期预测稳定性。相比以往方法，预测精度提升16.3%–64.2%。所提出的多尺度增量建模为推动人机交互中的动作预测能力提供了强有力的技术支持。
原文摘要 · Abstract (English)
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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