| Autor: |
Gomes, Daniel Fernandes, Mou, Wenxuan, Paoletti, Paolo, Luo, Shan |
| Rok vydání: |
2023 |
| Předmět: |
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| Zdroj: |
5th UK Robot Manipulation Workshop 2024 |
| Druh dokumentu: |
Working Paper |
| Popis: |
End-to-end self-supervised models have been proposed for estimating the success of future candidate grasps and video predictive models for generating future observations. However, none have yet studied these two strategies side-by-side for addressing the aforementioned grasping problem. We investigate and compare a model-free approach, to estimate the success of a candidate grasp, against a model-based alternative that exploits a self-supervised learnt predictive model that generates a future observation of the gripper about to grasp an object. Our experiments demonstrate that despite the end-to-end model-free model obtaining a best accuracy of 72%, the proposed model-based pipeline yields a significantly higher accuracy of 82%. |
| Databáze: |
arXiv |
| Externí odkaz: |
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