Drifting Gaussian Processes with Varying Neighborhood Sizes for Online Model Learning

Computationally efficient online learning of nonstationary models remains a difficult challenge. A robust and reliable algorithm could have great impact on problems in learning control. Recent work on combining the worlds of computationally efficient and locally adaptive learning algorithms with robust learning frameworks such as Gaussian process regression has taken a step towards both robust and real-time capable learning systems. However, online learning of model parameters on streaming data -that is strongly correlated, such as data arriving along a trajectory -can still create serious issues for many learning systems. Here we investigate the idea of drifting Gaussian processes which explicitly exploit the fact that data is generated along trajectories. A drifting Gaussian process keeps a history of a constant number of recently observed data points and updates its hyper-parameters at each time step. Instead of optimizing the neighborhood size on which the GP is trained on, we propose to use several -in parallel -drifting GPs whose predictions are combined for query points. We illustrate our approach on synthetically generated data and successfully evaluate on inverse dynamics learning tasks.