Sampling-based methods for factored task and motion planning
Name
1801.00680.pdf
Description
Accepted version
Size
2.32 MB
Format
Adobe PDF
Checksum (MD5)
b76b9467cff8ce745cac5fbba916bac5
Author(s)
Garrett, Caelan Reed
Lozano-Pérez, Tomás
Kaelbling, Leslie P
Date Issued
October 10, 2018
Journal
International Journal of Robotics Research
Publisher
SAGE Publications
Citation
Garrett, Caelan Reed, Lozano-Pérez, Tomás and Kaelbling, Leslie Pack. "Sampling-based methods for factored task and motion planning." International Journal of Robotics Research 37, 13/14 (December 2018): 1796-1825 © The Author(s) 2018.
Version
Author's final manuscript
Abstract
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. Keywords: task and motion planning; manipulation planning; AI reasoning
MIT Department
Massachusetts Institute of Technology. Computer Science and Artificial Intelligence Laboratory
Terms of Use
Creative Commons Attribution-Noncommercial-Share Alike
Persistent DSpace Link
DOI of Published Version
https://doi.org/10.1177/0278364918802962
