A State-Based Regression Formulation for Domains with Sensing Actions<br> and Incomplete Information

We present a state-based regression function for planning domains where an agent does not have complete information and may have sensing actions. We consider binary domains and employ a three-valued characterization of domains with sensing actions to define the regression function. We prove the soundness and completeness of our regression formulation with respect to the definition of progression. More specifically, we show that (i) a plan obtained through regression for a planning problem is indeed a progression solution of that planning problem, and that (ii) for each plan found through progression, using regression one obtains that plan or an equivalent one.Comment: 34 pages, 7 Figure

Integrated robot task and motion planning in belief space

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.This work was supported in part by the NSF under Grant No. 1117325. Any opinions, findings, and conclusions or recommendations expressed in this material are those of the author(s) and do not necessarily reflect the views of the National Science Foundation. We also gratefully acknowledge support from ONR MURI grant N00014-09-1-1051, from AFOSR grant AOARD-104135 and from the Singapore Ministry of Education under a grant to the Singapore-MIT International Design Center. We thank Willow Garage for the use of the PR2 robot as part of the PR2 Beta Program

A State-Based Regression Formulation for Domains with Sensing Actions and Incomplete Information

We present a state-based regression function for planning domains where an agent does not have complete information and may have sensing actions. We consider binary domains and employ a three-valued characterization of domains with sensing actions to define the regression function. We prove the soundness and completeness of our regression formulation with respect to the definition of progression. More specifically, we show that (i) a plan obtained through regression for a planning problem is indeed a progression solution of that planning problem, and that (ii) for each plan found through progression, using regression one obtains that plan or an equivalent one

A State-Based Regression Formulation for Domains with Sensing Actions and Incomplete Information

We present a state-based regression function for planning domains where anagent does not have complete information and may have sensing actions. Weconsider binary domains and employ a three-valued characterization of domainswith sensing actions to define the regression function. We prove the soundnessand completeness of our regression formulation with respect to the definitionof progression. More specifically, we show that (i) a plan obtained throughregression for a planning problem is indeed a progression solution of thatplanning problem, and that (ii) for each plan found through progression, usingregression one obtains that plan or an equivalent one.Comment: 34 pages, 7 Figure