Reasoning from sequences of raw sensory data is a ubiquitous problem across fields ranging from medical devices to robotics. These problems often involve using long sequences of raw sensor data (e.g. magnetometers, piezoresistors) to predict sequences of desirable physical quantities (e.g. force, inertial measurements). While classical approaches are powerful for locally-linear prediction problems, they often fall short when using real-world sensors. These sensors are typically non-linear, are affected by extraneous variables (e.g. vibration), and exhibit data-dependent drift. For many problems, the prediction task is exacerbated by small labeled datasets since obtaining ground-truth labels requires expensive equipment. In this work, we present Hierarchical State-Space Models (HiSS), a conceptually simple, new technique for continuous sequential prediction. HiSS stacks structured state-space models on top of each other to create a temporal hierarchy. Across six real-world sensor datasets, from tactile-based state prediction to accelerometer-based inertial measurement, HiSS outperforms state-of-the-art sequence models such as causal Transformers, LSTMs, S4, and Mamba by at least 23% on MSE. Our experiments further indicate that HiSS demonstrates efficient scaling to smaller datasets and is compatible with existing data-filtering techniques. Code, datasets and videos can be found on https://hiss-csp.github.io.

翻译：从原始传感器数据序列中进行推理是跨领域（从医疗设备到机器人技术）的普遍问题。这些问题通常涉及利用长序列原始传感器数据（如磁力计、压阻传感器）来预测所需物理量（如力、惯性测量）的序列。尽管经典方法在局部线性预测问题上表现强大，但在处理真实世界传感器时往往力不从心。这些传感器通常是非线性的，受外部变量（如振动）影响，并表现出数据依赖的漂移。对于许多问题，由于获取真实标签需要昂贵设备，小规模标注数据集进一步加剧了预测任务的难度。本文提出层次状态空间模型（HiSS），一种概念简单的新型连续序列预测技术。HiSS通过堆叠结构化状态空间模型形成时间层级结构。在六个真实传感器数据集上（从基于触觉的状态预测到基于加速度计的惯性测量），HiSS相比因果Transformer、LSTM、S4和Mamba等最先进序列模型，均方误差（MSE）至少降低23%。我们的实验进一步表明，HiSS对小数据集具有高效扩展性，且兼容现有数据滤波技术。代码、数据集和视频可访问https://hiss-csp.github.io获取。