  Recent learning-to-plan methods have shown promising results on planning
directly from observation space. Yet, their ability to plan for long-horizon
tasks is limited by the accuracy of the prediction model. On the other hand,
classical symbolic planners show remarkable capabilities in solving
long-horizon tasks, but they require predefined symbolic rules and symbolic
states, restricting their real-world applicability. In this work, we combine
the benefits of these two paradigms and propose a learning-to-plan method that
can directly generate a long-term symbolic plan conditioned on high-dimensional
observations. We borrow the idea of regression (backward) planning from
classical planning literature and introduce Regression Planning Networks (RPN),
a neural network architecture that plans backward starting at a task goal and
generates a sequence of intermediate goals that reaches the current
observation. We show that our model not only inherits many favorable traits
from symbolic planning, e.g., the ability to solve previously unseen tasks but
also can learn from visual inputs in an end-to-end manner. We evaluate the
capabilities of RPN in a grid world environment and a simulated 3D kitchen
environment featuring complex visual scenes and long task horizons, and show
that it achieves near-optimal performance in completely new task instances.
