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MP
2006
75views more  MP 2006»
13 years 7 months ago
A Class of stochastic programs with decision dependent uncertainty
We address a class of problems where decisions have to be optimized over a time horizon given that the future is uncertain and that the optimization decisions influence the time o...
Vikas Goel, Ignacio E. Grossmann
ICRA
2007
IEEE
134views Robotics» more  ICRA 2007»
14 years 1 months ago
Grasping POMDPs
Abstract— We provide a method for planning under uncertainty for robotic manipulation by partitioning the configuration space into a set of regions that are closed under complia...
Kaijen Hsiao, Leslie Pack Kaelbling, Tomás ...
ICRA
2007
IEEE
126views Robotics» more  ICRA 2007»
14 years 1 months ago
A formal framework for robot learning and control under model uncertainty
— While the Partially Observable Markov Decision Process (POMDP) provides a formal framework for the problem of robot control under uncertainty, it typically assumes a known and ...
Robin Jaulmes, Joelle Pineau, Doina Precup
PAMI
2007
186views more  PAMI 2007»
13 years 7 months ago
Value-Directed Human Behavior Analysis from Video Using Partially Observable Markov Decision Processes
—This paper presents a method for learning decision theoretic models of human behaviors from video data. Our system learns relationships between the movements of a person, the co...
Jesse Hoey, James J. Little
RSS
2007
136views Robotics» more  RSS 2007»
13 years 9 months ago
The Stochastic Motion Roadmap: A Sampling Framework for Planning with Markov Motion Uncertainty
— We present a new motion planning framework that explicitly considers uncertainty in robot motion to maximize the probability of avoiding collisions and successfully reaching a ...
Ron Alterovitz, Thierry Siméon, Kenneth Y. ...