mlsanityData to 7 Oct 2026

Visual Memory for Robust Path Following

arXiv 1812.00940 ↗First posted 3 Dec 2018cs.CV cs.LG cs.ROcited by 16

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Abstract (arXiv)

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.

Key facts. Cited by 16 papers in the map (data through 7 Oct 2026). First posted on arXiv 3 Dec 2018.

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Citing papers per year: 2021: 3; 2022: 4; 2023: 0; 2024: 5; 2025: 2; 2026: 2.

16 papers.

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