  We consider the problem of spatial path planning. In contrast to the
classical solutions which optimize a new plan from scratch and assume access to
the full map with ground truth obstacle locations, we learn a planner from the
data in a differentiable manner that allows us to leverage statistical
regularities from past data. We propose Spatial Planning Transformers (SPT),
which given an obstacle map learns to generate actions by planning over
long-range spatial dependencies, unlike prior data-driven planners that
propagate information locally via convolutional structure in an iterative
manner. In the setting where the ground truth map is not known to the agent, we
leverage pre-trained SPTs in an end-to-end framework that has the structure of
mapper and planner built into it which allows seamless generalization to
out-of-distribution maps and goals. SPTs outperform prior state-of-the-art
differentiable planners across all the setups for both manipulation and
navigation tasks, leading to an absolute improvement of 7-19%.

Machine Learning;Artificial Intelligence;Computer Vision and Pattern Recognition;Robotics