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» Load estimation and control using learned dynamics models
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UAI
2003
13 years 9 months ago
Learning Continuous Time Bayesian Networks
Continuous time Bayesian networks (CTBN) describe structured stochastic processes with finitely many states that evolve over continuous time. A CTBN is a directed (possibly cycli...
Uri Nodelman, Christian R. Shelton, Daphne Koller
ICRA
2009
IEEE
132views Robotics» more  ICRA 2009»
14 years 2 months ago
Smoothed Sarsa: Reinforcement learning for robot delivery tasks
— Our goal in this work is to make high level decisions for mobile robots. In particular, given a queue of prioritized object delivery tasks, we wish to find a sequence of actio...
Deepak Ramachandran, Rakesh Gupta
MICCAI
2008
Springer
14 years 1 months ago
Soft Tissue Tracking for Minimally Invasive Surgery: Learning Local Deformation Online
Accurate estimation and tracking of dynamic tissue deformation is important to motion compensation, intra-operative surgical guidance and navigation in minimally invasive surgery. ...
Peter Mountney and Guang-Zhong Yang
IROS
2008
IEEE
144views Robotics» more  IROS 2008»
14 years 2 months ago
Learning nonparametric policies by imitation
— A long cherished goal in artificial intelligence has been the ability to endow a robot with the capacity to learn and generalize skills from watching a human teacher. Such an ...
David B. Grimes, Rajesh P. N. Rao
ICIP
2007
IEEE
14 years 9 months ago
Video Content Representation by Incremental Non-Negative Matrix Factorization
Nonnegative Matrix Factorization (NMF) is a powerful decomposition tool which has been used in several content representation applications recently. However, there are some diffic...
Bilge Günsel, Serhat Selcuk Bucak