Integrated task and motion planning in belief space

We describe an integrated strategy for planning, perception, state estimation and action in complex mobile manipulation domains based on planning in the belief space of probability distributions over states using hierarchical goal regression (pre-image back-chaining). We develop a vocabulary of logi...

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Bibliographic Details
Published in:The International journal of robotics research 2013-08, Vol.32 (9-10), p.1194-1227
Main Authors: Kaelbling, Leslie Pack, Lozano-Pérez, Tomás
Format: Article
Language:English
Subjects:
Mathematical problems
Perception
Probability distribution
Regression
Regression analysis
Robotics
Robots
Simulation
State estimation
Strategy
Tasks
Uncertainty
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Summary:We describe an integrated strategy for planning, perception, state estimation and action in complex mobile manipulation domains based on planning in the belief space of probability distributions over states using hierarchical goal regression (pre-image back-chaining). We develop a vocabulary of logical expressions 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 can give rise 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.
ISSN:0278-3649
1741-3176
DOI:10.1177/0278364913484072