  The problem of path planning has been studied for years. Classic planning
pipelines, including perception, mapping, and path searching, can result in
latency and compounding errors between modules. While recent studies have
demonstrated the effectiveness of end-to-end learning methods in achieving high
planning efficiency, these methods often struggle to match the generalization
abilities of classic approaches in handling different environments. Moreover,
end-to-end training of policies often requires a large number of labeled data
or training iterations to reach convergence. In this paper, we present a novel
Imperative Learning (IL) approach. This approach leverages a differentiable
cost map to provide implicit supervision during policy training, eliminating
the need for demonstrations or labeled trajectories. Furthermore, the policy
training adopts a Bi-Level Optimization (BLO) process, which combines network
update and metric-based trajectory optimization, to generate a smooth and
collision-free path toward the goal based on a single depth measurement. The
proposed method allows task-level costs of predicted trajectories to be
backpropagated through all components to update the network through direct
gradient descent. In our experiments, the method demonstrates around 4x faster
planning than the classic approach and robustness against localization noise.
Additionally, the IL approach enables the planner to generalize to various
unseen environments, resulting in an overall 26-87% improvement in SPL
performance compared to baseline learning methods.
