Sample-Based Methods for Factored Task and Motion Planning
© 2017 MIT Press Journals. All rights reserved. There has been a great deal of progress in developing probabilistically complete methods that move beyond motion planning to multi-modal problems including various forms of task planning. This paper presents a general-purpose formulation of a large cla...
| Main Authors: | , , |
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| Format: | Article |
| Language: | English |
| Published: |
Robotics: Science and Systems Foundation,
2021-11-08T16:28:52Z.
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| Subjects: | |
| Online Access: | Get fulltext |
| Summary: | © 2017 MIT Press Journals. All rights reserved. There has been a great deal of progress in developing probabilistically complete methods that move beyond motion planning to multi-modal problems including various forms of task planning. This paper presents a general-purpose formulation of a large class of discrete-time planning problems, with hybrid state and action spaces. The formulation characterizes conditions on the submanifolds in which solutions lie, leading to a characterization of robust feasibility that incorporates dimensionality-reducing constraints. It then connects those conditions to corresponding conditional samplers that are provided as part of a domain specification. We present domain-independent sample-based planning algorithms and show that they are both probabilistically complete and computationally efficient on a set of challenging benchmark problems. |
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