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KVP: A knowledge of volumes approach to robot task planning
, 2013
"... Abstract — Robot task planning is an inherently challenging problem, as it covers both continuous-space geometric reasoning about robot motion and perception, as well as purely symbolic knowledge about actions and objects. This paper presents a novel “knowledge of volumes ” framework for solving gen ..."
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Cited by 11 (8 self)
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Abstract — Robot task planning is an inherently challenging problem, as it covers both continuous-space geometric reasoning about robot motion and perception, as well as purely symbolic knowledge about actions and objects. This paper presents a novel “knowledge of volumes ” framework for solving generic robot tasks in partially known environments. In particular, this approach (abbreviated, KVP) combines the power of symbolic, knowledge-level AI planning with the efficient computation of volumes, which serve as an intermediate representation for both robot action and perception. While we demonstrate the effectiveness of our framework in a bimanual robot bartender scenario, our approach is also more generally applicable to tasks in automation and mobile manipulation, involving arbitrary numbers of manipulators. I.
Improving Planner Performance in Grid Worlds with Macro Actions
"... Abstract. In this paper we explore a class of grid world planning domains that models high-level multi-robot navigation in confined spaces, and that gives rise to certain problem instances over which some modern planning techniques perform surprisingly poorly. We show that the inclusion of macro act ..."
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Abstract. In this paper we explore a class of grid world planning domains that models high-level multi-robot navigation in confined spaces, and that gives rise to certain problem instances over which some modern planning techniques perform surprisingly poorly. We show that the inclusion of macro actions, inspired by techniques that humans use to solve similar problems, allows planners to find solu-tions in some cases where they would otherwise fail. We then show that the pattern exploited to create the macro actions could poten-tially be exploited in the majority of planning domains used in the International Planning Competition. 1
