Journal
INTERNATIONAL JOURNAL OF ROBOTICS RESEARCH
Volume 37, Issue 13-14, Pages 1796-1825Publisher
SAGE PUBLICATIONS LTD
DOI: 10.1177/0278364918802962
Keywords
task and motion planning; manipulation planning; AI reasoning
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Funding
- NSF [1420316, 1523767, 1723381]
- AFOSR [FA9550-17-1-0165]
- ONR [N00014-14-1-0486]
- NSF GRFP fellowship [1122374]
- Direct For Computer & Info Scie & Enginr
- Div Of Information & Intelligent Systems [1420316] Funding Source: National Science Foundation
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This paper presents a general-purpose formulation of a large class of discrete-time planning problems, with hybrid state and control-spaces, as factored transition systems. Factoring allows state transitions to be described as the intersection of several constraints each affecting a subset of the state and control variables. Robotic manipulation problems with many movable objects involve constraints that only affect several variables at a time and therefore exhibit large amounts of factoring. We develop a theoretical framework for solving factored transition systems with sampling-based algorithms. The framework characterizes conditions on the submanifold in which solutions lie, leading to a characterization of robust feasibility that incorporates dimensionality-reducing constraints. It then connects those conditions to corresponding conditional samplers that can be composed to produce values on this submanifold. We present two domain-independent, probabilistically complete planning algorithms that take, as input, a set of conditional samplers. We demonstrate the empirical efficiency of these algorithms on a set of challenging task and motion planning problems involving picking, placing, and pushing.
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