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.
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Proceedings ArticleDOI
Autonomous Automobile Trajectory Tracking for Off-Road Driving: Controller Design, Experimental Validation and Racing
TL;DR: This work treats automobile trajectory tracking in a new manner, by considering the orientation of the front wheels - not the vehicle's body - with respect to the desired trajectory, enabling collocated control of the system.
Towards Personal Service Robots for the Elderly
Gregory Baltus,Dieter Fox,Francine Gemperle,Jennifer L. Goetz,Tad Hirsch,Dimitris Magaritis,Michael Montemerlo,Joelle Pineau,Nicholas Roy,Jamie Schulte,Sebastian Thrun +10 more
TL;DR: The state-of-the art of a large-scale project, aimed towards the development of personal service robots for the elderly population, is described, which develops a first prototype robot that can provide information related to activities of daily living obtained from the Web.
Proceedings ArticleDOI
Experiences with a mobile robotic guide for the elderly
TL;DR: An implemented robot system, which relies heavily on probabilistic AI techniques for acting under uncertainty, and successfully demonstrated that it could autonomously provide guidance for elderly residents in an assisted living facility.
Efficient Exploration In Reinforcement Learning
TL;DR: It is proved that for all finite deterministic domains, reinforcement learning using a directed technique can always be performed in polynomial time, demonstrating the important role of exploration in reinforcement learning.
Proceedings Article
Monte Carlo POMDPs
TL;DR: A Monte Carlo algorithm for learning to act in partially observable Markov decision processes (POMDPs) with real-valued state and action spaces using importance sampling for representing beliefs, and Monte Carlo approximation for belief propagation.