# Modeling of AUV Dynamics with Limited Resources: Efficient Online Learning Using Uncertainty

arXiv: https://arxiv.org/abs/2504.04583 · HTML: https://mlsanity.com/paper/2504.04583/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Builds on 11 of its 35 references that are in the map.
- First posted on arXiv 6 Apr 2025.

## Builds on (most cited first, 11 of 11)

- [Dropout as a Bayesian Approximation: Representing Model Uncertainty in Deep Learning](https://mlsanity.com/paper/1506.02142/) (2015)
- [Simple and Scalable Predictive Uncertainty Estimation using Deep Ensembles](https://mlsanity.com/paper/1612.01474/) (2016)
- [Flipout: Efficient Pseudo-Independent Weight Perturbations on Mini-Batches](https://mlsanity.com/paper/1803.04386/) (2018)
- [A Deeper Look into Aleatoric and Epistemic Uncertainty Disentanglement](https://mlsanity.com/paper/2204.09308/) (2022)
- [Rehearsal revealed: The limits and merits of revisiting samples in continual learning](https://mlsanity.com/paper/2104.07446/) (2021)
- [Incremental Learning In Online Scenario](https://mlsanity.com/paper/2003.13191/) (2020)
- [Online Active Regression](https://mlsanity.com/paper/2207.05945/) (2022)
- [A Framework for On-line Learning of Underwater Vehicles Dynamic Models](https://mlsanity.com/paper/1903.05355/) (2019)
- [Exploring the Limits of Epistemic Uncertainty Quantification in Low-Shot Settings](https://mlsanity.com/paper/2111.09808/) (2021)
- [Dynamic System Identification of Underwater Vehicles Using Multi-Output Gaussian Processes](https://mlsanity.com/paper/2006.02194/) (2020)
- [An ensemble of online estimation methods for one degree-of-freedom models of unmanned surface vehicles: applied theory and preliminary field results with eight vehicles](https://mlsanity.com/paper/2308.00782/) (2023)

## Built on by (most cited first, 1 of 1)

- [A Differentiable Composite Approximation Framework for Autonomous Underwater Vehicle Maneuvering Modeling from Sea-Trial Data](https://mlsanity.com/paper/2606.19711/) (2026)

## Abstract (arXiv)

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.