Integrated robot task and motion planning in belief space
Name
MIT-CSAIL-TR-2012-019.pdf
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2.34 MB
Format
Adobe PDF
Checksum (MD5)
1aaacafd6ae62673650f35b67a7b4a39
Author(s)
Kaelbling, Leslie Pack
Lozano-Perez, Tomas
Advisor(s)
Leslie Kaelbling
Tomas Lozano-Perez
Date Issued
July 3, 2012
Series/Report no.
MIT-CSAIL-TR-2012-019
Abstract
In this paper, we describe an integrated strategy for planning, perception, state-estimation and action in complex mobile manipulation domains. The strategy is based on planning in the belief space of probability distribution over states. Our planning approach is based on hierarchical goal regression (pre-image back-chaining). We develop a vocabulary of fluents that describe sets of belief states, which are goals and subgoals in the planning process. We show that a relatively small set of symbolic operators lead to task-oriented perception in support of the manipulation goals. An implementation of this method is demonstrated in simulation and on a real PR2 robot, showing robust, flexible solution of mobile manipulation problems with multiple objects and substantial uncertainty.
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Creative Commons Attribution-NonCommercial-NoDerivs 3.0 Unported
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