用不确定性筛选数据，让水下机器人在有限存储下高效更新动力学模型。
Modeling of AUV Dynamics with Limited Resources: Efficient Online Learning Using Uncertainty
- 基于不确定性选择关键数据点，减少冗余存储
- 阈值法稳定性强，累计测试损失最低
- 适合资源受限的实时在线学习场景
机器学习在构建水下航行器动力学模型方面表现优异，但持续使用数据流更新模型时，常需存储大量冗余数据。本文研究在存储受限条件下，利用不确定性选择重训数据点的方法。采用多层感知机集成模型预测认知不确定性。提出三种新方法：阈值法（剔除不确定性低于阈值的样本）、贪心法（最大化存储点的不确定性）和阈值-贪心法（结合两者）。在水下车辆Dagon采集的数据上评估，结果表明阈值法在整个学习过程中更稳定，且累积测试损失最低。还分析了模型参数与存储容量对性能的影响，并对比了三种不确定性估计方法的效果。
原文摘要 · Abstract (English)
Machine learning proves effective in constructing dynamics models from data, especially for underwater vehicles. Continuous refinement of these models using incoming data streams, however, often requires storage of an overwhelming amount of redundant data. This work investigates the use of uncertainty in the selection of data points to rehearse in online learning when storage capacity is constrained. The models are learned using an ensemble of multilayer perceptrons as they perform well at predicting epistemic uncertainty. We present three novel approaches: the Threshold method, which excludes samples with uncertainty below a specified threshold, the Greedy method, designed to maximize uncertainty among the stored points, and Threshold-Greedy, which combines the previous two approaches. The methods are assessed on data collected by an underwater vehicle Dagon. Comparison with baselines reveals that the Threshold exhibits enhanced stability throughout the learning process and also yields a model with the least cumulative testing loss. We also conducted detailed analyses on the impact of model parameters and storage size on the performance of the models, as well as a comparison of three different uncertainty estimation methods.
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