  Applications of Reinforcement Learning (RL) in robotics are often limited by
high data demand. On the other hand, approximate models are readily available
in many robotics scenarios, making model-based approaches like planning a
data-efficient alternative. Still, the performance of these methods suffers if
the model is imprecise or wrong. In this sense, the respective strengths and
weaknesses of RL and model-based planners are. In the present work, we
investigate how both approaches can be integrated into one framework that
combines their strengths. We introduce Learning to Execute (L2E), which
leverages information contained in approximate plans to learn universal
policies that are conditioned on plans. In our robotic manipulation
experiments, L2E exhibits increased performance when compared to pure RL, pure
planning, or baseline methods combining learning and planning.
