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AIPS
2006
13 years 7 months ago
Combining Stochastic Task Models with Reinforcement Learning for Dynamic Scheduling
We view dynamic scheduling as a sequential decision problem. Firstly, we introduce a generalized planning operator, the stochastic task model (STM), which predicts the effects of ...
Malcolm J. A. Strens
IJRR
2011
218views more  IJRR 2011»
13 years 17 days ago
Motion planning under uncertainty for robotic tasks with long time horizons
Abstract Partially observable Markov decision processes (POMDPs) are a principled mathematical framework for planning under uncertainty, a crucial capability for reliable operation...
Hanna Kurniawati, Yanzhu Du, David Hsu, Wee Sun Le...
HRI
2006
ACM
13 years 11 months ago
Effective team-driven multi-model motion tracking
Autonomous robots use sensors to perceive and track objects in the world. Tracking algorithms use object motion models to estimate the position of a moving object. Tracking effic...
Yang Gu, Manuela M. Veloso
RAS
2006
111views more  RAS 2006»
13 years 5 months ago
Planning under uncertainty using model predictive control for information gathering
This paper considers trajectory planning problems for autonomous robots in information gathering tasks. The objective of the planning is to maximize the information gathered withi...
Cindy Leung, Shoudong Huang, Ngai Ming Kwok, Gamin...
ICPR
2010
IEEE
13 years 3 months ago
Online Next-Best-View Planning for Accuracy Optimization Using an Extended E-Criterion
Next-best-view (NBV) planning is an important aspect for three-dimensional (3D) reconstruction within controlled environments, such as a camera mounted on a robotic arm. NBV metho...
Michael Trummer, Christoph Munkelt, Joachim Denzle...