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Learning the dynamics of robots from data can help achieve more accuratetracking controllers, or aid their navigation algorithms. However, when theactual dynamics of the robots change due to external conditions, on-lineadaptation of their models is required to maintain high fidelity performance.In this work, a framework for on-line learning of robot dynamics is developedto adapt to such changes. The proposed framework employs an incremental supportvector regression method to learn the model sequentially from data streams. Incombination with the incremental learning, strategies for including andforgetting data are developed to obtain better generalization over the wholestate space. The framework is tested in simulation and real experimentalscenarios demonstrating its adaptation capabilities to changes in the robot'sdynamics.

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