# A Framework for On-line Learning of Underwater Vehicles Dynamic Models

arXiv: https://arxiv.org/abs/1903.05355 · HTML: https://mlsanity.com/paper/1903.05355/

## Key facts

- Cited by 4 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 13 Mar 2019.

## Built on by (most cited first, 4 of 4)

- [Continual Learning for Real-World Autonomous Systems: Algorithms, Challenges and Frameworks](https://mlsanity.com/paper/2105.12374/) (2021)
- [Exploring the Intersection between Neural Architecture Search and Continual Learning](https://mlsanity.com/paper/2206.05625/) (2022)
- [Modeling of AUV Dynamics with Limited Resources: Efficient Online Learning Using Uncertainty](https://mlsanity.com/paper/2504.04583/) (2025)
- [Contextual Multi-Task Reinforcement Learning for Autonomous Reef Monitoring](https://mlsanity.com/paper/2604.12645/) (2026)

## Abstract (arXiv)

Learning the dynamics of robots from data can help achieve more accurate tracking controllers, or aid their navigation algorithms. However, when the actual dynamics of the robots change due to external conditions, on-line adaptation of their models is required to maintain high fidelity performance. In this work, a framework for on-line learning of robot dynamics is developed to adapt to such changes. The proposed framework employs an incremental support vector regression method to learn the model sequentially from data streams. In combination with the incremental learning, strategies for including and forgetting data are developed to obtain better generalization over the whole state space. The framework is tested in simulation and real experimental scenarios demonstrating its adaptation capabilities to changes in the robot's dynamics.