Before revision:  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:  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 is often infeasible. We describe an approach for sim-to-real training that can accomplish unseen robotic tasks using models learned in simulation to ground components of a simple task planner. We learn a library of parameterized skills, along with a set of predicates-based preconditions and termination conditions, entirely in simulation .