GBPP: Grasp-Aware Base Placement Prediction for Robots via Two-Stage Learning
Fuente:
arXiv
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| Auteurs principaux: | , , , |
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| Format: | Preprint |
| Publié: |
2025
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| _version_ | 1866916952012750848 |
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| author | Chen, Jizhuo Liu, Diwen Wang, Jiaming Soh, Harold |
| author_facet | Chen, Jizhuo Liu, Diwen Wang, Jiaming Soh, Harold |
| contents | GBPP is a fast learning based scorer that selects a robot base pose for grasping from a single RGB-D snapshot. The method uses a two stage curriculum: (1) a simple distance-visibility rule auto-labels a large dataset at low cost; and (2) a smaller set of high fidelity simulation trials refines the model to match true grasp outcomes. A PointNet++ style point cloud encoder with an MLP scores dense grids of candidate poses, enabling rapid online selection without full task-and-motion optimization. In simulation and on a real mobile manipulator, GBPP outperforms proximity and geometry only baselines, choosing safer and more reachable stances and degrading gracefully when wrong. The results offer a practical recipe for data efficient, geometry aware base placement: use inexpensive heuristics for coverage, then calibrate with targeted simulation. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2509_11594 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | GBPP: Grasp-Aware Base Placement Prediction for Robots via Two-Stage Learning Chen, Jizhuo Liu, Diwen Wang, Jiaming Soh, Harold Robotics Artificial Intelligence GBPP is a fast learning based scorer that selects a robot base pose for grasping from a single RGB-D snapshot. The method uses a two stage curriculum: (1) a simple distance-visibility rule auto-labels a large dataset at low cost; and (2) a smaller set of high fidelity simulation trials refines the model to match true grasp outcomes. A PointNet++ style point cloud encoder with an MLP scores dense grids of candidate poses, enabling rapid online selection without full task-and-motion optimization. In simulation and on a real mobile manipulator, GBPP outperforms proximity and geometry only baselines, choosing safer and more reachable stances and degrading gracefully when wrong. The results offer a practical recipe for data efficient, geometry aware base placement: use inexpensive heuristics for coverage, then calibrate with targeted simulation. |
| title | GBPP: Grasp-Aware Base Placement Prediction for Robots via Two-Stage Learning |
| topic | Robotics Artificial Intelligence |
| url | https://arxiv.org/abs/2509.11594 |