The invention relates to guided uncertainty-aware policy optimization: combining model-free and model-based strategies for sample-efficient learning. A robot is controlled using a combination of model-based and model-free control methods. In some examples, the model-based method uses a physical model of the environment around the robot to guide the robot. The physical model is oriented using a perception system such as a camera. Characteristics of the perception system may be are used to determine an uncertainty for the model. Based at least in part on this uncertainty, the system transitions from the model-based method to a model-free method where, in some embodiments, information provided directly from the perception system is used to direct the robot without reliance on the physical model.
GUIDED UNCERTAINTY-AWARE POLICY OPTIMIZATION: COMBINING MODEL-FREE AND MODEL-BASED STRATEGIES FOR SAMPLE-EFFICIENT LEARNING