Robotics: Science and Systems XIII

Sample-Based Methods for Factored Task and Motion Planning

Caelan Garrett, Tomas Lozano-Perez, Leslie Kaelbling


There has been a great deal of progress in developing probabilistically complete methods that move beyond motion planning to multi-modal problems including various forms of task planning. This paper presents a general-purpose formulation of a large class of discrete-time planning problems, with hybrid state and action spaces. The formulation characterizes conditions on the submanifolds 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 are provided as part of a domain specification. We present domain-independent sample-based planning algorithms and show that they are both probabilistically complete and computationally efficient on a set of challenging benchmark problems.



    AUTHOR    = {Caelan Garrett AND Tomas Lozano-Perez AND Leslie Kaelbling}, 
    TITLE     = {Sample-Based Methods for Factored Task and Motion Planning}, 
    BOOKTITLE = {Proceedings of Robotics: Science and Systems}, 
    YEAR      = {2017}, 
    ADDRESS   = {Cambridge, Massachusetts}, 
    MONTH     = {July}, 
    DOI       = {10.15607/RSS.2017.XIII.039}