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.
@article{arxiv.1903.05355,
title = {A Framework for On-line Learning of Underwater Vehicles Dynamic Models},
author = {Bilal Wehbe and Marc Hildebrandt and Frank Kirchner},
journal= {arXiv preprint arXiv:1903.05355},
year = {2019}
}