Learning to guide task and motion planning using score-space representation
Abstract
In this paper, we propose a learning algorithm that speeds up the search in task and motion planning problems. Our algorithm proposes solutions to three different challenges that arise in learning to improve planning efficiency: what to predict, how to represent a planning problem instance, and how to transfer knowledge from one problem instance to another. We propose a method that predicts constraints on the search space based on a generic representation of a planning problem instance, called score-space, where we represent a problem instance in terms of the performance of a set of solutions attempted so far. Using this representation, we transfer knowledge, in the form of constraints, from previous problems based on the similarity in score-space. We design a sequential algorithm that efficiently predicts these constraints, and evaluate it in three different challenging task and motion planning problems. Results indicate that our approach performs orders of magnitudes faster than an unguided planner.
Document Details
- Document Type
- Pub Defense Publication
- Publication Date
- May 13, 2019
- Source ID
- 10.1177/0278364919848837
Entities
People
- Beomjoon Kim
- Leslie P. Kaelbling
- Tomas Lozano-Pérez
- Zi Wang
Organizations
- Air Force Office of Scientific Research
- Charles Stark Draper Laboratory
- Massachusetts Institute of Technology
- National Science Foundation