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Traditional robotic approaches rely on an accurate model of the environment,a detailed description of how to perform the task, and a robust perceptionsystem to keep track of the current state. On the other hand, reinforcementlearning approaches can operate directly from raw sensory inputs with only areward signal to describe the task, but are extremely sample-inefficient andbrittle. In this work, we combine the strengths of model-based methods with theflexibility of learning-based methods to obtain a general method that is ableto overcome inaccuracies in the robotics perception/actuation pipeline, whilerequiring minimal interactions with the environment. This is achieved byleveraging uncertainty estimates to divide the space in regions where the givenmodel-based policy is reliable, and regions where it may have flaws or not bewell defined. In these uncertain regions, we show that a locally learned-policycan be used directly with raw sensory inputs. We test our algorithm, GuidedUncertainty-Aware Policy Optimization (GUAPO), on a real-world robot performingpeg insertion. Videos are available at https://sites.google.com/view/guapo-rl

Guided Uncertainty-Aware Policy Optimization: Combining Learning and Model-Based Strategies for Sample-Efficient Policy Learning