Before revision:  The main contribution of this paper is a novel cost function that guarantees collision-free shortest paths are found at its minimum. We show that our approach works seamlessly with RGBD input and predicts high-quality paths in 2D, 3D, and 6 DoF robotic manipulator settings. Our method also reduces training and inference time compared to existing approaches, in some cases by orders of magnitude .
After revision:  The main contribution of this paper is a novel cost function that 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 .