GoalGrasp: Grasping Goals in Partially Occluded Scenarios without Grasp Training
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arXiv
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| Auteurs principaux: | , , |
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| Format: | Preprint |
| Publié: |
2024
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| _version_ | 1866916700222390272 |
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| author | Gui, Shun Gui, Kai Luximon, Yan |
| author_facet | Gui, Shun Gui, Kai Luximon, Yan |
| contents | Grasping user-specified objects is crucial for robotic assistants; however, most current 6-DoF grasp detection methods are object-agnostic, making it challenging to grasp specific targets from a scene. To achieve that, we present GoalGrasp, a simple yet effective 6-DoF robot grasp pose detection method that does not rely on grasp pose annotations and grasp training. By combining 3D bounding boxes and simple human grasp priors, our method introduces a novel paradigm for robot grasp pose detection. GoalGrasp's novelty is its swift grasping of user-specified objects and partial mitigation of occlusion issues. The experimental evaluation involves 18 common objects categorized into 7 classes. Our method generates dense grasp poses for 1000 scenes. We compare our method's grasp poses to existing approaches using a novel stability metric, demonstrating significantly higher grasp pose stability. In user-specified robot grasping tests, our method achieves a 94% success rate, and 92% under partial occlusion. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2405_04783 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | GoalGrasp: Grasping Goals in Partially Occluded Scenarios without Grasp Training Gui, Shun Gui, Kai Luximon, Yan Robotics Grasping user-specified objects is crucial for robotic assistants; however, most current 6-DoF grasp detection methods are object-agnostic, making it challenging to grasp specific targets from a scene. To achieve that, we present GoalGrasp, a simple yet effective 6-DoF robot grasp pose detection method that does not rely on grasp pose annotations and grasp training. By combining 3D bounding boxes and simple human grasp priors, our method introduces a novel paradigm for robot grasp pose detection. GoalGrasp's novelty is its swift grasping of user-specified objects and partial mitigation of occlusion issues. The experimental evaluation involves 18 common objects categorized into 7 classes. Our method generates dense grasp poses for 1000 scenes. We compare our method's grasp poses to existing approaches using a novel stability metric, demonstrating significantly higher grasp pose stability. In user-specified robot grasping tests, our method achieves a 94% success rate, and 92% under partial occlusion. |
| title | GoalGrasp: Grasping Goals in Partially Occluded Scenarios without Grasp Training |
| topic | Robotics |
| url | https://arxiv.org/abs/2405.04783 |