視覚記憶によるロバストな経路追従
Visual Memory for Robust Path Following
経路の実演から抽象表現を生成し、その記憶を用いてノイズや環境変化があっても往復で経路をたどる学習手法を提案した論文。
著者: Ashish Kumar, Saurabh Gupta, David Fouhey, Sergey Levine, Jitendra Malik
分類: cs.CV, cs.LG, cs.RO
原文アブストラクト
Humans routinely retrace paths in a novel environment both forwards and backwards despite uncertainty in their motion. This paper presents an approach for doing so. Given a demonstration of a path, a first network generates a path abstraction. Equipped with this abstraction, a second network observes the world and decides how to act to retrace the path under noisy actuation and a changing environment. The two networks are optimized end-to-end at training time. We evaluate the method in two realistic simulators, performing path following and homing under actuation noise and environmental changes. Our experiments show that our approach outperforms classical approaches and other learning based baselines.
