Title:
Hierarchical Decision Theoretic Planning for Navigation Among Movable Obstacles
Hierarchical Decision Theoretic Planning for Navigation Among Movable Obstacles
Author(s)
Levihn, Martin
Scholz, Jonathan
Stilman, Mike
Scholz, Jonathan
Stilman, Mike
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Abstract
In this paper we present the first decision theoretic planner for the problem
of Navigation Among Movable Obstacles (NAMO). While efficient planners
for NAMO exist, they are challenging to implement in practice due to the inherent
uncertainty in both perception and control of real robots. Generalizing existing
NAMO planners to nondeterministic domains is particularly difficult due to the sensitivity
of MDP methods to task dimensionality. Our work addresses this challenge
by combining ideas from Hierarchical Reinforcement Learning with Monte Carlo
Tree Search, and results in an algorithm that can be used for fast online planning
in uncertain environments. We evaluate our algorithm in simulation, and provide
a theoretical argument for our results which suggest linear time complexity in the
number of obstacles for typical environments.
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Date Issued
2012-06
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Proceedings