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Unifying perception, estimation and action for mobile manipulation via belief space planning

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2 Author(s)
Kaelbling, L.P. ; Comput. Sci. & Artificial Intell. Lab., Massachusetts Inst. of Technol., Cambridge, MA, USA ; Lozano-Perez, T.

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 symbolic 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.

Published in:

Robotics and Automation (ICRA), 2012 IEEE International Conference on

Date of Conference:

14-18 May 2012