  Robotic systems must be able to quickly and robustly make decisions when
operating in uncertain and dynamic environments. While Reinforcement Learning
(RL) can be used to compute optimal policies with little prior knowledge about
the environment, it suffers from slow convergence. An alternative approach is
Model Predictive Control (MPC), which optimizes policies quickly, but also
requires accurate models of the system dynamics and environment. In this paper
we propose a new approach, adaptive probabilistic trajectory optimization, that
combines the benefits of RL and MPC. Our method uses scalable approximate
inference to learn and updates probabilistic models in an online incremental
fashion while also computing optimal control policies via successive local
approximations. We present two variations of our algorithm based on the Sparse
Spectrum Gaussian Process (SSGP) model, and we test our algorithm on three
learning tasks, demonstrating the effectiveness and efficiency of our approach.
