Learning Robust Grasping Strategy Through Tactile Sensing and Adaption Skill
Fuente:
arXiv
Saved in:
| Main Authors: | , , , , , |
|---|---|
| Format: | Preprint |
| Published: |
2024
|
| Subjects: | |
| Online Access: | |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
| _version_ | 1866912128406913024 |
|---|---|
| author | Hu, Yueming Li, Mengde Yang, Songhua Li, Xuetao Liu, Sheng Li, Miao |
| author_facet | Hu, Yueming Li, Mengde Yang, Songhua Li, Xuetao Liu, Sheng Li, Miao |
| contents | Robust grasping represents an essential task in robotics, necessitating tactile feedback and reactive grasping adjustments for robust grasping of objects. Previous research has extensively combined tactile sensing with grasping, primarily relying on rule-based approaches, frequently neglecting post-grasping difficulties such as external disruptions or inherent uncertainties of the object's physics and geometry. To address these limitations, this paper introduces an human-demonstration-based adaptive grasping policy base on tactile, which aims to achieve robust gripping while resisting disturbances to maintain grasp stability. Our trained model generalizes to daily objects with seven different sizes, shapes, and textures. Experimental results demonstrate that our method performs well in dynamic and force interaction tasks and exhibits excellent generalization ability. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2411_08499 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | Learning Robust Grasping Strategy Through Tactile Sensing and Adaption Skill Hu, Yueming Li, Mengde Yang, Songhua Li, Xuetao Liu, Sheng Li, Miao Robotics Robust grasping represents an essential task in robotics, necessitating tactile feedback and reactive grasping adjustments for robust grasping of objects. Previous research has extensively combined tactile sensing with grasping, primarily relying on rule-based approaches, frequently neglecting post-grasping difficulties such as external disruptions or inherent uncertainties of the object's physics and geometry. To address these limitations, this paper introduces an human-demonstration-based adaptive grasping policy base on tactile, which aims to achieve robust gripping while resisting disturbances to maintain grasp stability. Our trained model generalizes to daily objects with seven different sizes, shapes, and textures. Experimental results demonstrate that our method performs well in dynamic and force interaction tasks and exhibits excellent generalization ability. |
| title | Learning Robust Grasping Strategy Through Tactile Sensing and Adaption Skill |
| topic | Robotics |
| url | https://arxiv.org/abs/2411.08499 |