PDDLStream: Integrating symbolic planners and blackbox samplers via optimistic adaptive planning
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 co...
| Main Authors: | , , |
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| Other Authors: | |
| Format: | Article |
| Language: | English |
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Association for the Advancement of Artificial Intelligence (AAAI)
2021
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| Online Access: | https://hdl.handle.net/1721.1/130316 |
| _version_ | 1811076066109816832 |
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| author | Garrett, Caelan Reed Lozano-Pérez, Tomás Kaelbling, Leslie P |
| author2 | Massachusetts Institute of Technology. Computer Science and Artificial Intelligence Laboratory |
| author_facet | Massachusetts Institute of Technology. Computer Science and Artificial Intelligence Laboratory Garrett, Caelan Reed Lozano-Pérez, Tomás Kaelbling, Leslie P |
| author_sort | Garrett, Caelan Reed |
| collection | MIT |
| description | 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. |
| first_indexed | 2024-09-23T10:15:33Z |
| format | Article |
| id | mit-1721.1/130316 |
| institution | Massachusetts Institute of Technology |
| language | English |
| last_indexed | 2024-09-23T10:15:33Z |
| publishDate | 2021 |
| publisher | Association for the Advancement of Artificial Intelligence (AAAI) |
| record_format | dspace |
| spelling | mit-1721.1/1303162022-09-26T16:49:58Z PDDLStream: Integrating symbolic planners and blackbox samplers via optimistic adaptive planning Garrett, Caelan Reed Lozano-Pérez, Tomás Kaelbling, Leslie P Massachusetts Institute of Technology. Computer Science and Artificial Intelligence Laboratory 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. NSF (Grants 1523767 and 1723381) AFOSR (Grant FA9550-17-1-0165) ONR (Grant N00014-18-1-2847) 2021-03-31T20:41:43Z 2021-03-31T20:41:43Z 2020-10 2020-12-22T19:09:02Z Article http://purl.org/eprint/type/ConferencePaper 978-1-57735-824-4 2334-0843 2334-0835 https://hdl.handle.net/1721.1/130316 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 en https://ojs.aaai.org/index.php/ICAPS/article/view/6739 Proceedings of the Thirtieth International Conference on Automated Planning and Scheduling Creative Commons Attribution-Noncommercial-Share Alike http://creativecommons.org/licenses/by-nc-sa/4.0/ application/pdf Association for the Advancement of Artificial Intelligence (AAAI) arXiv |
| spellingShingle | Garrett, Caelan Reed Lozano-Pérez, Tomás Kaelbling, Leslie P PDDLStream: Integrating symbolic planners and blackbox samplers via optimistic adaptive planning |
| title | PDDLStream: Integrating symbolic planners and blackbox samplers via optimistic adaptive planning |
| title_full | PDDLStream: Integrating symbolic planners and blackbox samplers via optimistic adaptive planning |
| title_fullStr | PDDLStream: Integrating symbolic planners and blackbox samplers via optimistic adaptive planning |
| title_full_unstemmed | PDDLStream: Integrating symbolic planners and blackbox samplers via optimistic adaptive planning |
| title_short | PDDLStream: Integrating symbolic planners and blackbox samplers via optimistic adaptive planning |
| title_sort | pddlstream integrating symbolic planners and blackbox samplers via optimistic adaptive planning |
| url | https://hdl.handle.net/1721.1/130316 |
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