Chao Tang1,2, Dehao Huang1,2, Wenqi Ge1,2, Weiyu Liu3, Hong Zhang1,2

[1] Shenzhen Key Laboratory of Robotics and Computer Vision, SUSTech, Shenzhen, China.

[2] Department of Electronic and Electrical Engineering, SUSTech, Shenzhen, China.

[3]  Institute for Robotics and Intelligent Machines, Georgia Institute of Technology, Atlanta, United States.

Abstract: Task-oriented grasping (TOG) refers to the problem of predicting grasps on an object that enable subsequent manipulation tasks. To model the complex relationships between objects, tasks, and grasps, existing methods incorporate semantic knowledge as priors into TOG pipelines. However, the existing semantic knowledge is typically constructed based on closed-world concept sets, restraining the generalization to novel concepts out of the pre-defined sets. To address this issue, we propose GraspGPT, a large language model (LLM) based TOG framework that leverages the open-end semantic knowledge from an LLM to achieve zero-shot generalization to novel concepts. We conduct experiments on Language Augmented TaskGrasp (LA-TaskGrasp) dataset and demonstrate that GraspGPT outperforms existing TOG methods on different held-out settings when generalizing to novel concepts out of the training set. The effectiveness of GraspGPT is further validated in real-robot experiments. 

@article{tang2023graspgpt,

  title={GraspGPT: Leveraging Semantic Knowledge from a Large Language Model for Task-Oriented Grasping},

  author={Tang, Chao and Huang, Dehao and Ge, Wenqi and Liu, Weiyu and Zhang, Hong},

  journal={arXiv preprint arXiv:2307.13204},

  year={2023}

}