Saved in:
| Main Authors: | , , , |
|---|---|
| Format: | Preprint |
| Published: |
2023
|
| Subjects: | |
| Online Access: | https://arxiv.org/abs/2403.07877 |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
| _version_ | 1866914711310696448 |
|---|---|
| author | Gomes, Daniel Fernandes Mou, Wenxuan Paoletti, Paolo Luo, Shan |
| author_facet | Gomes, Daniel Fernandes Mou, Wenxuan Paoletti, Paolo Luo, Shan |
| contents | 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%. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2403_07877 |
| institution | arXiv |
| publishDate | 2023 |
| record_format | arxiv |
| spellingShingle | Generating Future Observations to Estimate Grasp Success in Cluttered Environments Gomes, Daniel Fernandes Mou, Wenxuan Paoletti, Paolo Luo, Shan Robotics Computer Vision and Pattern Recognition 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%. |
| title | Generating Future Observations to Estimate Grasp Success in Cluttered Environments |
| topic | Robotics Computer Vision and Pattern Recognition |
| url | https://arxiv.org/abs/2403.07877 |