Schaub, Henry and Wolff, Christian and Hoh, Maximilian and Schoettl, Alfred (2024) Probabilistic Closed-Loop Active Grasping. IEEE ROBOTICS AND AUTOMATION LETTERS, 9 (4). pp. 3964-3971. ISSN 2377-3766
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Picking a specific object is an essential task of assistive robotics. While the majority of grasp detection approaches focus on grasp synthesis from a single depth image or point cloud, this approach is often not viable in an unstructured, uncontrolled environment. Due to occlusion, heavy influence of noise or simply because no collision-free grasp is visible from some perspectives, it is beneficial to collect additional information from other views before opting for grasp execution. We present a closed-loop approach that selects and navigates towards the next-best-view by minimizing the entropy of the volume under consideration. We use a local measure of estimation uncertainty of the surface reconstruction, to sample grasps and estimate their success probabilities in an online fashion. Our experiments show that our algorithm achieves better grasp success rates than comparable approaches, when presented with challenging household objects.
| Item Type: | Article |
|---|---|
| Uncontrolled Keywords: | RECONSTRUCTION; Robot sensing systems; Estimation; Grasping; Probabilistic logic; Noise measurement; Entropy; Uncertainty; Assistive robots; manipulators; robot motion; robot sensing systems |
| Subjects: | 000 Computer science, information & general works > 004 Computer science 600 Technology > 600 Technology (Applied sciences) |
| Divisions: | Languages and Literatures > Institut für Information und Medien, Sprache und Kultur (I:IMSK) > Lehrstuhl für Medieninformatik (Prof. Dr. Christian Wolff) Informatics and Data Science > Department Human-Centered Computing > Lehrstuhl für Medieninformatik (Prof. Dr. Christian Wolff) |
| Depositing User: | Dr. Gernot Deinzer |
| Date Deposited: | 03 Dec 2025 07:57 |
| Last Modified: | 03 Dec 2025 07:57 |
| URI: | https://pred.uni-regensburg.de/id/eprint/64581 |
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