経路計画arXiv:2011.14787
教師なし経路回帰ネットワーク
Unsupervised Path Regression Networks
正解の最適経路を使わずに、ニューラルネットワークから直接スプライン回帰で衝突のない最短経路を求める手法を提案した論文。
著者: Michal Pándy, Daniel Lenton, Ronald Clark
分類: cs.RO, cs.CV, cs.LG
原文アブストラクト
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.
