Summary of Text2grasp: Grasp Synthesis by Text Prompts Of Object Grasping Parts, By Xiaoyun Chang and Yi Sun
Text2Grasp: Grasp synthesis by text prompts of object grasping parts
by Xiaoyun Chang, Yi Sun
First submitted to arxiv on: 9 Apr 2024
Categories
- Main: Artificial Intelligence (cs.AI)
- Secondary: None
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| Summary difficulty | Written by | Summary |
|---|---|---|
| High | Paper authors | High Difficulty Summary Read the original abstract here |
| Medium | GrooveSquid.com (original content) | Medium Difficulty Summary The proposed Text2Grasp method enables more precise grasp synthesis through text prompts of object grasping parts, addressing the ambiguity faced by existing methods using human intention or task-level language. A two-stage approach is presented, comprising a text-guided diffusion model (TextGraspDiff) to generate coarse grasp poses and a hand-object contact optimization process for ensuring plausibility and diversity. Leveraging Large Language Models facilitates grasp synthesis guided by task-level and personalized text descriptions without additional manual annotations. The method achieves accurate part-level grasp control and comparable performance in grasp quality. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary Text2Grasp is a new way to help robots pick up objects using written instructions. Normally, robots use human intentions or general language to figure out how to grab things. But this can be unclear. To solve this problem, scientists created a method that uses text prompts about what parts of an object to grasp. This makes it more precise and accurate. They also used large language models to help with personalized instructions without needing more human work. |
Keywords
» Artificial intelligence » Diffusion model » Optimization
