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 Article
Integrating inductive neural network learning and explanation-based learning
Sebastian Thrun,Tom M. Mitchell +1 more
TL;DR: A learning method that combines explanation-based learning from a previously learned approximate domain theory, together with inductive learning from observations, based on a neural network representation of domain knowledge that is robust to errors in the domain theory.
Proceedings ArticleDOI
PAO for planning with hidden state
TL;DR: A heuristic search algorithm for generating optimal plans in a new class of decision problem, characterised by the incorporation of hidden state, is described, which interleaves heuristic expansion of the reduced space with forwards and backwards propagation phases to produce a solution in a fraction of the time required by other techniques.
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A Probabilistic Framework for Real-time 3D Segmentation using Spatial, Temporal, and Semantic Cues.
TL;DR: A probabilistic 3D segmentation method that combines spatial, temporal, and semantic information to make better-informed decisions about how to segment a scene and is able to significantly reduce both undersegmentations and oversegmentations on the KITTI dataset while still running in real-time.
Proceedings ArticleDOI
Autonomous driving in semi-structured environments: Mapping and planning
Dmitri A. Dolgov,Sebastian Thrun +1 more
TL;DR: This work addresses the problem of autonomous driving in semi-structured environments with strong topological structure, demonstrating robust estimation of lane networks in parking lots and the benefits of using these topological networks to guide path planning.
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Jacobian images of super-resolved texture maps for model-based motion estimation and tracking
TL;DR: A key result is the notion of Jacobian images, which can be viewed as a generalization of traditional gradient images, and represent the crucial computation in the tracking process.