GeoMatch++: Morphology Conditioned Geometry Matching for Multi-Embodiment Grasping
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_ | 1866916543395266560 |
|---|---|
| author | Wei, Yunze Attarian, Maria Gilitschenski, Igor |
| author_facet | Wei, Yunze Attarian, Maria Gilitschenski, Igor |
| contents | Despite recent progress on multi-finger dexterous grasping, current methods focus on single grippers and unseen objects, and even the ones that explore cross-embodiment, often fail to generalize well to unseen end-effectors. This work addresses the problem of dexterous grasping generalization to unseen end-effectors via a unified policy that learns correlation between gripper morphology and object geometry. Robot morphology contains rich information representing how joints and links connect and move with respect to each other and thus, we leverage it through attention to learn better end-effector geometry features. Our experiments show an average of 9.64% increase in grasp success rate across 3 out-of-domain end-effectors compared to previous methods. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2412_18998 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | GeoMatch++: Morphology Conditioned Geometry Matching for Multi-Embodiment Grasping Wei, Yunze Attarian, Maria Gilitschenski, Igor Robotics Despite recent progress on multi-finger dexterous grasping, current methods focus on single grippers and unseen objects, and even the ones that explore cross-embodiment, often fail to generalize well to unseen end-effectors. This work addresses the problem of dexterous grasping generalization to unseen end-effectors via a unified policy that learns correlation between gripper morphology and object geometry. Robot morphology contains rich information representing how joints and links connect and move with respect to each other and thus, we leverage it through attention to learn better end-effector geometry features. Our experiments show an average of 9.64% increase in grasp success rate across 3 out-of-domain end-effectors compared to previous methods. |
| title | GeoMatch++: Morphology Conditioned Geometry Matching for Multi-Embodiment Grasping |
| topic | Robotics |
| url | https://arxiv.org/abs/2412.18998 |