Planning under Uncertainty for Robotic Tasks with Mixed Observability

  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.},
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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