预测人类机器人行走中突发跌倒，用视觉与体感数据提升预测准确率。
A Multimodal Label Forecasting Method for Aperiodic Visuo-Motor Time Series
- 融合内生与外生变量的新型深度模型，支持非周期性运动预测
- 真实数据上性能提升12.73%以上，仿真数据上提升10.40%以上
- 专为无周期性运动设计，适合机器人安全预警等场景
近年来，深度学习在时间序列预测（TSF）中应用日益广泛。基于Transformer和MLP的模型在多个真实世界TSF回归基准上表现良好，但关于哪种方法更优仍存在争议。值得注意的是，当前多数数据集和方法假设时间序列具有近似周期性。本文聚焦于一种无周期性的新任务：基于第一人称视觉与本体感觉，预测人形机器人行走中的突发跌倒。当运动轨迹足够多样化时，周期性被打破。我们构建了两个新基准数据集（一个来自仿真，一个来自真实硬件），证明周期性不成立，且现有深度TSF方法在此类任务上表现不佳。我们提出一种新型深度学习架构，同时利用内生与外生变量，并采用严格独立同分布采样的训练过程。实验结果表明，在多种条件下均显著优于现有方法：真实数据上提升12.73%以上，仿真数据上提升10.40%以上。代码与数据集将在论文接收后公开。
原文摘要 · Abstract (English)
Deep learning models have been increasingly applied to Time Series Forecasting (TSF) in recent years. Transformer-based and MLP-based models have both been used effectively on many real-world TSF regression benchmarks, and there is ongoing debate as to which family of methods is best. While these benchmarks have drawn much attention, it is also worth noting that many current datasets and methods assume approximate periodicity in the time series. In this work, we focus on a new TSF task without periodicity: anticipating falls during humanoid locomotion, on the basis of egocentric vision and proprioception. When the locomotion trajectories are sufficiently diverse, periodicity is violated. We contribute two new benchmark datasets (one from simulation, one from real hardware), showing that periodicity is violated and recent deep TSF methods struggle on these benchmarks. We also propose a novel deep learning architecture that exploits both endogenous and exogenous variables and a training process that rigorously enforces i.i.d sampling of training examples. Our results show statistically significant improvement over prior art in multiple experimental conditions, by 12.73% or more on the real data and 10.40% or more on the simulation data. Code and datasets will be available upon acceptance.
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