Caelan Reed Garrett, Tomás Lozano-Pérez, Leslie Pack Kaelbling |
PosterID:
36
PDF
Slides
Poster
BibTeX
|
| 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. | |
| Canb | 10/28/2020, 10:00 – 11:00 |
| 10/30/2020, 03:00 – 04:00 |
| Paris | 10/28/2020, 00:00 – 01:00 |
| 10/29/2020, 17:00 – 18:00 |
| NYC | 10/27/2020, 19:00 – 20:00 |
| 10/29/2020, 12:00 – 13:00 |
| LA | 10/27/2020, 16:00 – 17:00 |
| 10/29/2020, 09:00 – 10:00 |
A Simple and Fast Bi-Objective Search Algorithm
Carlos Hernández Ulloa, William Yeoh, Jorge A. Baier, Han Zhang, Luis Suazo, Sven Koenig
Predicting the Effectiveness of Bidirectional Heuristic Search
Nathan R. Sturtevant, Shahaf Shperberg, Ariel Felner, Jingwei Chen
Sequencing Operator Counts with State-Space Search
Wesley L. Kaizer, André G. Pereira, Marcus Ritt
PDDLStream: Integrating Symbolic Planners and Blackbox Samplers via Optimistic Adaptive Planning
Caelan Reed Garrett, Tomás Lozano-Pérez, Leslie Pack Kaelbling