連續序列對序列建模的階層狀態空間模型

Hierarchical State Space Models for Continuous Sequence-to-Sequence Modeling

February 15, 2024

作者: Raunaq Bhirangi, Chenyu Wang, Venkatesh Pattabiraman, Carmel Majidi, Abhinav Gupta, Tess Hellebrekers, Lerrel Pinto

cs.AI

摘要

從原始感測數據序列進行推理是一個普遍存在的問題，涵蓋範圍從醫療設備到機器人技術。這些問題通常涉及使用長序列的原始感測數據（例如磁力計、壓阻器）來預測理想物理量的序列（例如力量、慣性測量）。儘管傳統方法對於局部線性預測問題很有效，但在使用真實世界的感測器時往往表現不佳。這些感測器通常是非線性的，受到外部變量（例如振動）的影響，並呈現出依賴數據的漂移。對於許多問題來說，由於獲取地面真實標籤需要昂貴的設備，預測任務變得更加困難。在這項工作中，我們提出了分層狀態空間模型（HiSS），這是一種概念上簡單的新技術，用於連續序列預測。HiSS將結構化的狀態空間模型堆疊在一起，以創建時間層次結構。在從基於觸覺的狀態預測到基於加速度計的慣性測量等六個真實世界感測器數據集上，HiSS在均方誤差（MSE）上至少比因果Transformer、LSTM、S4和Mamba等最先進的序列模型表現優異23%。我們的實驗進一步表明，HiSS對較小數據集的有效擴展性，並與現有的數據過濾技術兼容。代碼、數據集和視頻可在https://hiss-csp.github.io找到。

English

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.