S
Sebastian Thrun
Researcher at Stanford University
Publications - 437
Citations - 108035
Sebastian Thrun is an academic researcher from Stanford University. The author has contributed to research in topics: Mobile robot & Robot. The author has an hindex of 146, co-authored 434 publications receiving 98124 citations. Previous affiliations of Sebastian Thrun include University of Pittsburgh & ETH Zurich.
Papers
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Proceedings ArticleDOI
Towards 3D object recognition via classification of arbitrary object tracks
TL;DR: This paper presents a new track classification method, based on a mathematically principled method of combining log odds estimators, that is fast enough for real time use, is non-specific to object class, and performs well on the task of classifying correctly-tracked, well-segmented objects into car, pedestrian, bicyclist, and background classes.
Finding and Optimizing Solvable Priority Schemes for Decoupled Path Planning Techniques for Teams of Mobile Robots.
TL;DR: In this article, a method for finding and optimizing priority schemes for such decoupled and prioritized planning techniques is presented, which performs a randomized search with hill-climbing to find solutions and to minimize the overall path length.
Journal ArticleDOI
Autonomous exploration and mapping of abandoned mines
Sebastian Thrun,Scott M. Thayer,William Whittaker,Christopher R. Baker,Wolfram Burgard,David I. Ferguson,Dirk Hähnel,D. Montemerlo,A. Morris,Zachary Omohundro,Carlos F. Reverte +10 more
TL;DR: The software architecture of an autonomous robotic system designed to explore and map abandoned mines and some of the challenges that arise in the subterranean environments and some the difficulties of building truly autonomous robots are discussed.
Proceedings Article
Finding Structure in Reinforcement Learning
Sebastian Thrun,Anton Schwartz +1 more
TL;DR: SKILLS discovers skills, which are partially defined action policies that arise in the context of multiple, related tasks, that are learned by minimizing the compactness of action policies, using a description length argument on their representation.
Proceedings Article
Monte Carlo Localization with Mixture Proposal Distribution
TL;DR: Experimental results with physical robots and an analysis of the formulation of a new proposal distribution for the Monte Carlo sampling step suggest that the new algorithm is significantly more robust and accurate than plain MCL.