Summary of Semgrasp: Semantic Grasp Generation Via Language Aligned Discretization, by Kailin Li et al.
SemGrasp: Semantic Grasp Generation via Language Aligned Discretization
by Kailin Li, Jingbo Wang, Lixin Yang, Cewu Lu, Bo Dai
First submitted to arxiv on: 4 Apr 2024
Categories
- Main: Computer Vision and Pattern Recognition (cs.CV)
- Secondary: Artificial Intelligence (cs.AI)
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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 A novel semantic-based grasp generation method called SemGrasp is introduced, which generates a static human grasp pose by incorporating semantic information into the grasp representation. The approach aligns the grasp space with semantic space, allowing for the generation of grasp postures in accordance with language instructions. A Multimodal Large Language Model (MLLM) is fine-tuned to integrate object, grasp, and language within a unified semantic space. The method is trained using a large-scale dataset called CapGrasp, featuring about 260k detailed captions and 50k diverse grasps. Experimental results show that SemGrasp efficiently generates natural human grasps aligned with linguistic intentions. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary A new way to create robot hands that can understand what we’re saying has been developed. This method uses words and pictures together to make the robot’s hand movements match what we mean. It’s a big improvement over previous methods that only used the shape of objects to decide how to grasp them. The system is tested with lots of different words, objects, and hand positions to see if it works well. It does! |
Keywords
» Artificial intelligence » Large language model
