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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Journal ArticleDOI
SCAPE: shape completion and animation of people
TL;DR: The SCAPE method is capable of constructing a high-quality animated surface model of a moving person, with realistic muscle deformation, using just a single static scan and a marker motion capture sequence of the person.
Proceedings ArticleDOI
Monte Carlo localization for mobile robots
TL;DR: The Monte Carlo localization method is introduced, where the probability density is represented by maintaining a set of samples that are randomly drawn from it, and it is shown that the resulting method is able to efficiently localize a mobile robot without knowledge of its starting location.
Book
Robotic mapping: a survey
TL;DR: This article provides a comprehensive introduction into the field of robotic mapping, with a focus on indoor mapping, and describes and compares various probabilistic techniques, as they are presently being applied to a vast array of mobile robot mapping problems.
Journal ArticleDOI
Substrate Elasticity Regulates Skeletal Muscle Stem Cell Self-Renewal in Culture
Penney M. Gilbert,Karen Havenstrite,Klas E. G. Magnusson,Klas E. G. Magnusson,Alessandra Sacco,Nora Leonardi,Nora Leonardi,Peggy E. Kraft,N. K. Nguyen,Sebastian Thrun,Matthias P. Lutolf,Helen M. Blau +11 more
TL;DR: Using a bioengineered substrate to recapitulate key biophysical and biochemical niche features in conjunction with a highly automated single-cell tracking algorithm, it is shown that substrate elasticity is a potent regulator of MuSC fate in culture.
BookDOI
Learning to learn
Sebastian Thrun,Lorien Pratt +1 more
TL;DR: This chapter discusses Reinforcement Learning with Self-Modifying Policies J. Schmidhuber, et al., and theoretical Models of Learning to Learn J. Baxter, a first step towards Continual Learning.