Before revision: Zero-shot execution of unseen robotic tasks is an important problem in robotics. One potential approach is through task planning: combining known skills based on their preconditions and effects to achieve a user-specified goal. In this work, we propose such a task planning approach to build a reactive system for multi-step manipulation tasks that can be trained on simulation data and applied in the real-world  .
After revision: Zero-shot execution of unseen robotic tasks is important to allowing robots to perform a wide variety of tasks in human environments, but collecting the amounts of data necessary to train end-to-end policies in the real-world .