  We demonstrate that challenging shortest path problems can be solved via
direct spline regression from a neural network, trained in an unsupervised
manner (i.e. without requiring ground truth optimal paths for training). To
achieve this, we derive a geometry-dependent optimal cost function whose minima
guarantees collision-free solutions. Our method beats state-of-the-art
supervised learning baselines for shortest path planning, with a much more
scalable training pipeline, and a significant speedup in inference time.
