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We propose an analytic moment-based filter for nonlinear stochastic dynamical systems modeled by Gaussian processes. Exact expressions for the expected value and the covariance matrix are provided for both the prediction and the filter step, where an additional Gaussian assumption is exploited in th ...

Autonomous learning has been a promising direction in control and robotics for more than a decade since learning models and controllers from data allows us to reduce the amount of engineering knowledge that is otherwise required. Due to their flexibility, autonomous reinforcement learning (RL) appro ...

Efficient skill acquisition is crucial for creating versatile robots. One intuitive way to teach a robot new tricks is to enable it to match its behavior to a teacher's demonstration of the task at hand. This approach is known as imitation learning. Classical methods of imitation learning su ...

To scale Gaussian processes (GPs) to large data sets we introduce the robust Bayesian Committee Machine (rBCM), a practical and scalable product-of-experts model for large-scale distributed GP regression. Unlike state-of-the-art sparse GP approximations, the rBCM is conceptually simple and does not ...