Before revision:  Importantly , the generator is learned to directly maximize the down-stream planning performance. We evaluate MAGIC on several long-term planning tasks , showing that it significantly outperforms planning using primitive actions and hand-crafted macro-actions in both simulation and on a real robot  .
After revision:  Importantly , the generator generates on the fly situation-aware macro-actions conditioned on the robot's belief and the environment context. We evaluated MAGIC on several long-term planning tasks , showing that it significantly outperforms planning using primitive actions and hand-crafted macro-actions in both simulation and on a real robot .