  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.

Robotics;Machine Learning;Systems and Control