Modeling of AUV Dynamics with Limited Resources: Efficient Online Learning Using Uncertainty
This work addresses the challenge of redundant data storage in continuous model refinement for autonomous underwater vehicles, presenting an incremental improvement in online learning efficiency.
This paper tackles the problem of efficiently refining dynamics models for underwater vehicles in online learning with limited storage by using uncertainty to select data points for rehearsal, finding that the Threshold method achieves the least cumulative testing loss and enhanced stability.
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.