Abstract
POMDPs provide a rich framework for planning and control in partially observable domains. Recent new algorithms have greatly improved the scalability of POMDPs, to the point where they can be used in robot applications. In this paper, we describe how approximate POMDP solving can be further improved by the use of a new theoretically-motivated algorithm for selecting salient information states. We present the algorithm, called PEMA, demonstrate competitive performance on a range of navigation tasks, and show how this approach is robust to mismatches between the robot’s physical environment and the model used for planning.
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Pineau, J., Gordon, G.J. (2007). POMDP Planning for Robust Robot Control. In: Thrun, S., Brooks, R., Durrant-Whyte, H. (eds) Robotics Research. Springer Tracts in Advanced Robotics, vol 28. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-48113-3_7
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DOI: https://doi.org/10.1007/978-3-540-48113-3_7
Publisher Name: Springer, Berlin, Heidelberg
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