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| Main Authors: | , , |
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| Format: | Preprint |
| Published: |
2024
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| Subjects: | |
| Online Access: | https://arxiv.org/abs/2408.05795 |
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| _version_ | 1866910563131457536 |
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| author | Tosello, Elisa Valentini, Alessandro Micheli, Andrea |
| author_facet | Tosello, Elisa Valentini, Alessandro Micheli, Andrea |
| contents | Task And Motion Planning (TAMP) is the problem of finding a solution to an automated planning problem that includes discrete actions executable by low-level continuous motions. This field is gaining increasing interest within the robotics community, as it significantly enhances robot's autonomy in real-world applications. Many solutions and formulations exist, but no clear standard representation has emerged. In this paper, we propose a general and open-source framework for modeling and benchmarking TAMP problems. Moreover, we introduce an innovative meta-technique to solve TAMP problems involving moving agents and multiple task-state-dependent obstacles. This approach enables using any off-the-shelf task planner and motion planner while leveraging a geometric analysis of the motion planner's search space to prune the task planner's exploration, enhancing its efficiency. We also show how to specialize this meta-engine for the case of an incremental SMT-based planner. We demonstrate the effectiveness of our approach across benchmark problems of increasing complexity, where robots must navigate environments with movable obstacles. Finally, we integrate state-of-the-art TAMP algorithms into our framework and compare their performance with our achievements. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2408_05795 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | A Meta-Engine Framework for Interleaved Task and Motion Planning using Topological Refinements Tosello, Elisa Valentini, Alessandro Micheli, Andrea Robotics Artificial Intelligence Task And Motion Planning (TAMP) is the problem of finding a solution to an automated planning problem that includes discrete actions executable by low-level continuous motions. This field is gaining increasing interest within the robotics community, as it significantly enhances robot's autonomy in real-world applications. Many solutions and formulations exist, but no clear standard representation has emerged. In this paper, we propose a general and open-source framework for modeling and benchmarking TAMP problems. Moreover, we introduce an innovative meta-technique to solve TAMP problems involving moving agents and multiple task-state-dependent obstacles. This approach enables using any off-the-shelf task planner and motion planner while leveraging a geometric analysis of the motion planner's search space to prune the task planner's exploration, enhancing its efficiency. We also show how to specialize this meta-engine for the case of an incremental SMT-based planner. We demonstrate the effectiveness of our approach across benchmark problems of increasing complexity, where robots must navigate environments with movable obstacles. Finally, we integrate state-of-the-art TAMP algorithms into our framework and compare their performance with our achievements. |
| title | A Meta-Engine Framework for Interleaved Task and Motion Planning using Topological Refinements |
| topic | Robotics Artificial Intelligence |
| url | https://arxiv.org/abs/2408.05795 |