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| Main Authors: | , , |
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| Format: | Preprint |
| Published: |
2025
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| Subjects: | |
| Online Access: | https://arxiv.org/abs/2503.04409 |
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| _version_ | 1866915184300261376 |
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| author | Tuncer, Cankut Bora Haliloglu, Dilruba Sultan Oguz, Ozgur S. |
| author_facet | Tuncer, Cankut Bora Haliloglu, Dilruba Sultan Oguz, Ozgur S. |
| contents | In this paper, we present SeGMan, a hybrid motion planning framework that integrates sampling-based and optimization-based techniques with a guided forward search to address complex, constrained sequential manipulation challenges, such as pick-and-place puzzles. SeGMan incorporates an adaptive subgoal selection method that adjusts the granularity of subgoals, enhancing overall efficiency. Furthermore, proposed generalizable heuristics guide the forward search in a more targeted manner. Extensive evaluations in maze-like tasks populated with numerous objects and obstacles demonstrate that SeGMan is capable of generating not only consistent and computationally efficient manipulation plans but also outperform state-of-the-art approaches. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2503_04409 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | SeGMan: Sequential and Guided Manipulation Planner for Robust Planning in 2D Constrained Environments Tuncer, Cankut Bora Haliloglu, Dilruba Sultan Oguz, Ozgur S. Robotics In this paper, we present SeGMan, a hybrid motion planning framework that integrates sampling-based and optimization-based techniques with a guided forward search to address complex, constrained sequential manipulation challenges, such as pick-and-place puzzles. SeGMan incorporates an adaptive subgoal selection method that adjusts the granularity of subgoals, enhancing overall efficiency. Furthermore, proposed generalizable heuristics guide the forward search in a more targeted manner. Extensive evaluations in maze-like tasks populated with numerous objects and obstacles demonstrate that SeGMan is capable of generating not only consistent and computationally efficient manipulation plans but also outperform state-of-the-art approaches. |
| title | SeGMan: Sequential and Guided Manipulation Planner for Robust Planning in 2D Constrained Environments |
| topic | Robotics |
| url | https://arxiv.org/abs/2503.04409 |