论文标题
无监督路径回归网络
Unsupervised Path Regression Networks
论文作者
论文摘要
我们证明，最短的路径问题可以通过从神经网络进行直接的样条回归解决，以无监督的方式进行训练（即不需要地面真理的最佳训练路径）。为了实现这一目标，我们得出了几何依赖性的最佳成本函数，其最小值保证了无碰撞解决方案。我们的方法击败了最新的监督学习基线，以进行最短的路径计划，并具有更可扩展的培训管道，并在推理时间上有了显着的加速。
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.
