Sampling-based methods for factored task and motion planning
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 st...
Main Authors: | Garrett, Caelan Reed (Author), Lozano-Pérez, Tomás (Author), Kaelbling, Leslie P (Author) |
---|---|
Other Authors: | Massachusetts Institute of Technology. Computer Science and Artificial Intelligence Laboratory (Contributor) |
Format: | Article |
Language: | English |
Published: |
SAGE Publications,
2020-04-06T14:34:53Z.
|
Subjects: | |
Online Access: | Get fulltext |
Similar Items
-
Sample-Based Methods for Factored Task and Motion Planning
by: Garrett, Caelan, et al.
Published: (2021) -
FFRob: An Efficient Heuristic for Task and Motion Planning
by: Garrett, Caelan Reed, et al.
Published: (2017) -
Active Model Learning and Diverse Action Sampling for Task and Motion Planning
by: Wang, Zi, et al.
Published: (2021) -
Learning compositional models of robot skills for task and motion planning
by: Wang, Zi, et al.
Published: (2022) -
Online Replanning in Belief Space for Partially Observable Task and Motion Problems
by: Garrett, Caelan Reed, et al.
Published: (2021)