Planning under Uncertainty for Robotic Tasks with Mixed Observability

@article{Ong2010PlanningUU,
  title={Planning under Uncertainty for Robotic Tasks with Mixed Observability},
  author={Sylvie C. W. Ong and Shao Wei Png and David Hsu and Wee Sun Lee},
  journal={I. J. Robotics Res.},
  year={2010},
  volume={29},
  pages={1053-1068}
}
Partially observable Markov decision processes (POMDPs) provide a principled, general framework for robot motion planning in uncertain and dynamic environments. They have been applied to various robotic tasks. However, solving POMDPs exactly is computationally intractable. A major challenge is to scale up POMDP algorithms for complex robotic tasks. Robotic systems often have mixed observability: even when a robot’s state is not fully observable, some components of the state may still be so. We… CONTINUE READING
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Point-based value iteration: An anytime algorithm for POMDPs

  • J. Pineau, G. Gordon, S. Thrun
  • Proc. Int. Jnt. Conf. on Artificial Intelligence…
  • 2003
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