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Stéphane Hug

dblp:82/1041 · DBLP profile ↗
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1ranked-venue papers
0as first author
0since 2021 · last 2000
—ORCID · none

Domains — the database's venue-derived domains; a paper can count in several

Artificial intelligence and machine learning · 1

Expertise — from the expertise taxonomy: the topics of the expert's papers under the CCF categories. A weight counts papers with recency: 1 for a paper about the topic, 0.3 when the topic is its context, halved every five years.

Artificial intelligence
1 paper
Robot navigation and mapping · 100%

Topics — the 3 heaviest of 3, each with the papers that count most for it

TopicWeightPapersLastEvidence papers
Robotics › Robot navigation and mapping
localization		 0.012000
Place Cells and Spatial Navigation Based on 2D Visual Feature Extraction, Path Integration, and Reinforcement Learning · NIPS 2000
Robotics › Robot navigation and mapping
spatial navigation		 0.012000
Place Cells and Spatial Navigation Based on 2D Visual Feature Extraction, Path Integration, and Reinforcement Learning · NIPS 2000
Robotics › Robot navigation and mapping › visual navigation
image-goal navigation		 0.012000
Place Cells and Spatial Navigation Based on 2D Visual Feature Extraction, Path Integration, and Reinforcement Learning · NIPS 2000

Methods — techniques the papers use, named apart from their topics

path integration · 0.0hebbian learning · 0.0gabor filter · 0.0
YearPublicationVenuePosition
2000 Place Cells and Spatial Navigation Based on 2D Visual Feature Extraction, Path Integration, and Reinforcement Learning
abstract
We model hippocampal place cells and head-direction cells by combin(cid:173) ing allothetic (visual) and idiothetic (proprioceptive) stimuli. Visual in(cid:173) put, provided by a video camera on a miniature robot, is preprocessed by a set of Gabor filters on 31 nodes of a log-polar retinotopic graph. Unsu(cid:173) pervised Hebbian learning is employed to incrementally build a popula(cid:173) tion of localized overlapping place fields. Place cells serve as basis func(cid:173) tions for reinforcement learning. Experimental results for goal-oriented navigation of a mobile robot are presented.
Angelo Arleo, Fabrizio Smeraldi, Stéphane Hug, Wulfram Gerstner
 NIPS3