轻量模型提升机器人交接中人体动作预测精度与速度
Enhancing Context-Aware Human Motion Prediction for Efficient Robot Handovers
- 用轻量架构siMLPe结合意图感知条件，提升预测效率
- 体位误差降低50%以上，推理速度提升200倍，参数仅需3%
- 适合实时人机协作场景，尤其对资源受限机器人有优势
精准的人体动作预测（HMP）对无缝人机协作至关重要，尤其在需要实时适应的交接任务中。尽管现有先进模型精度高，但计算复杂度限制了其在实际机器人应用中的部署。本文提出IntentMotion，基于轻量高效的siMLPe架构，引入意图感知条件、任务特异性损失函数及新型意图分类器，显著提升预测准确率同时保持高效。实验表明，该方法体位误差降低超过50%，推理速度提升200倍，参数量仅为现有最先进模型的3%。这些进展使本框架成为实时人机交互中高效可扩展的解决方案。
原文摘要 · Abstract (English)
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.
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