PDDLStream: Integrating symbolic planners and blackbox samplers via optimistic adaptive planning
Name
1802.08705.pdf
Description
Submitted version
Size
2.42 MB
Format
Adobe PDF
Checksum (MD5)
44410bfec57aef93b8aa4e014c8ffa13
Author(s)
Garrett, Caelan Reed
Lozano-Pérez, Tomás
Kaelbling, Leslie P
Date Issued
October 2020
Journal
Proceedings of the Thirtieth International Conference on Automated Planning and Scheduling
Publisher
Association for the Advancement of Artificial Intelligence (AAAI)
Citation
Garrett, Caelan Reed et al. "PDDLStream: Integrating symbolic planners and blackbox samplers via optimistic adaptive planning." Proceedings of the Thirtieth International Conference on Automated Planning and Scheduling, October 2020, Nancy, France, Association for the Advancement of Artificial Intelligence, October 2020. © 2020 Association for the Advancement of Artificial Intelligence
Version
Original manuscript
Abstract
Many planning applications involve complex relationships defined on high-dimensional, continuous variables. For example, robotic manipulation requires planning with kinematic, collision, visibility, and motion constraints involving robot configurations, object poses, and robot trajectories. These constraints typically require specialized procedures to sample satisfying values. We extend PDDL to support a generic, declarative specification for these procedures that treats their implementation as black boxes. We provide domain-independent algorithms that reduce PDDLStream problems to a sequence of finite PDDL problems. We also introduce an algorithm that dynamically balances exploring new candidate plans and exploiting existing ones. This enables the algorithm to greedily search the space of parameter bindings to more quickly solve tightly-constrained problems as well as locally optimize to produce low-cost solutions. We evaluate our algorithms on three simulated robotic planning domains as well as several real-world robotic tasks.
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://ojs.aaai.org/index.php/ICAPS/article/view/6739
