PDDLStream: Integrating symbolic planners and blackbox samplers via optimistic adaptive planning
Author(s)Garrett, Caelan Reed; Lozano-Pérez, Tomás; Kaelbling, Leslie P
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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.
DepartmentMassachusetts Institute of Technology. Computer Science and Artificial Intelligence Laboratory
Proceedings of the Thirtieth International Conference on Automated Planning and Scheduling
Association for the Advancement of Artificial Intelligence (AAAI)
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