http://underdog.stanford.edu/ 2026-04-08T22:00:12+00:00 http://underdog.stanford.edu/ http://underdog.stanford.edu/lib/images/favicon.ico text/html 2010-06-09T00:35:39+00:00 http://underdog.stanford.edu/doku.php?id=3d_vision&rev=1276043739&do=diff Overview Function approximation from noisy data is a central task in robot learning. Relevant problems include sensor modeling, manipulation, control, and many others. A large number of function approximation methods have been proposed from statistics, machine learning, and control system theory to address robotics-related issues such as online updates, active sampling, high dimensionality, non-homogeneous noise, and missing features. text/html 2010-06-14T17:11:18+00:00 http://underdog.stanford.edu/doku.php?id=contact&rev=1276535478&do=diff Office Visit us on Stanford Campus: Gates Computer Science Building 353 Serra Mall, Room 244 Stanford, CA 94305-9010 Contact Phone: +1 (650) 723-9558 Fax: +1 (650) 725-1449 E-Mail: plagemann@stanford.edu text/html 2010-06-14T20:18:27+00:00 http://underdog.stanford.edu/doku.php?id=home&rev=1276546707&do=diff Our research is focused on 3D perception for artificial systems. The goal is to develop the theoretical and practical fundamentals that will allow us to build seeing systems for dynamic and unconstrained environments. To this aim, we make use of the most recent sensor technology, such as 3D cameras, laser range finders or omnidirectional cameras and we draw inspiration from a wide range of scientific communities, including Computer Vision, Robotics, Machine Learning, Computer Graphics and Physi… text/html 2010-06-14T20:09:48+00:00 http://underdog.stanford.edu/doku.php?id=links&rev=1276546188&do=diff Stanford Resources * Artificial Intelligence Lab * Computer Science Department Related Research Groups (local) * DAGS, Daphne Koller * Driving Team, Sebastian Thrun * Vision Lab, Fei-Fei Li * Robot Learning Lab, Andrew Ng text/html 2009-01-20T19:08:47+00:00 http://underdog.stanford.edu/doku.php?id=orga&rev=1232478527&do=diff Organization * Christian Plagemann, Stanford University, <plagemann@stanford.edu> * Jo-Anne Ting, University of Southern California, <joanneti@usc.edu> * Sethu Vijayakumar, University of Edinburgh, <sethu.vijayakumar@ed.ac.uk> text/html 2011-02-23T20:53:29+00:00 http://underdog.stanford.edu/doku.php?id=people&rev=1298494409&do=diff Graduated Students * Alex Segal, <avsegal@stanford.edu> * Sameer Shariff, <shariff@stanford.edu> text/html 2010-06-07T18:54:06+00:00 http://underdog.stanford.edu/doku.php?id=program&rev=1275936846&do=diff T.B.A. text/html 2010-06-14T17:04:41+00:00 http://underdog.stanford.edu/doku.php?id=publications&rev=1276535081&do=diff text/html 2010-06-07T18:54:06+00:00 http://underdog.stanford.edu/doku.php?id=schedule&rev=1275936846&do=diff T.B.A. text/html 2009-01-20T18:56:09+00:00 http://underdog.stanford.edu/doku.php?id=start&rev=1232477769&do=diff Regression in Robotics -- Approaches and Applications Regression analysis, the modeling of functional dependencies between variables given noisy data samples, is a central task in many robot learning problems. Relevant problems in robotics include sensor modeling, manipulation learning, learning value functions for control, learning for planning, and many others. Many regression approaches from statistics and machine learning have been proposed to address robotics-related issues such as online… text/html 2009-01-20T23:50:56+00:00 http://underdog.stanford.edu/doku.php?id=workshop&rev=1232495456&do=diff RSS 2009 Workshop: Regression in Robotics -- Approaches and Applications Overview: Regression analysis, the modeling of functional dependencies between variables given noisy data samples, is a central task in many robot learning problems. Relevant problems in robotics include sensor modeling, manipulation learning, learning value functions for control, learning for planning, and many others. Many regression approaches from statistics and machine learning have been proposed to address robotic…