  Training long-horizon robotic policies in complex physical environments is
essential for many applications, such as robotic manipulation. However,
learning a policy that can generalize to unseen tasks is challenging. In this
work, we propose to achieve one-shot task generalization by decoupling plan
generation and plan execution. Specifically, our method solves complex
long-horizon tasks in three steps: build a paired abstract environment by
simplifying geometry and physics, generate abstract trajectories, and solve the
original task by an abstract-to-executable trajectory translator. In the
abstract environment, complex dynamics such as physical manipulation are
removed, making abstract trajectories easier to generate. However, this
introduces a large domain gap between abstract trajectories and the actual
executed trajectories as abstract trajectories lack low-level details and are
not aligned frame-to-frame with the executed trajectory. In a manner
reminiscent of language translation, our approach leverages a seq-to-seq model
to overcome the large domain gap between the abstract and executable
trajectories, enabling the low-level policy to follow the abstract trajectory.
Experimental results on various unseen long-horizon tasks with different robot
embodiments demonstrate the practicability of our methods to achieve one-shot
task generalization.
