# GATER: Learning Grasp-Action-Target Embeddings and Relations for Task-Specific Grasping

arXiv: https://arxiv.org/abs/2111.13815 · HTML: https://mlsanity.com/paper/2111.13815/

## Key facts

- Cited by 4 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 27 Nov 2021.

## Built on by (most cited first, 4 of 4)

- [A Joint Modeling of Vision-Language-Action for Target-oriented Grasping in Clutter](https://mlsanity.com/paper/2302.12610/) (2023)
- [Task-Oriented 6-DoF Grasp Pose Detection in Clutters](https://mlsanity.com/paper/2502.16976/) (2025)
- [LAC-Net: Linear-Fusion Attention-Guided Convolutional Network for Accurate Robotic Grasping Under the Occlusion](https://mlsanity.com/paper/2408.03238/) (2024)
- [QwenGrasp: A Usage of Large Vision-Language Model for Target-Oriented Grasping](https://mlsanity.com/paper/2309.16426/) (2023)

## Abstract (arXiv)

Intelligent service robots require the ability to perform a variety of tasks in dynamic environments. Despite the significant progress in robotic grasping, it is still a challenge for robots to decide grasping position when given different tasks in unstructured real life environments. In order to overcome this challenge, creating a proper knowledge representation framework is the key. Unlike the previous work, in this paper, task is defined as a triplet including grasping tool, desired action and target object. Our proposed algorithm GATER (Grasp--Action--Target Embeddings and Relations) models the relationship among grasping tools--action--target objects in embedding space. To validate our method, a novel dataset is created for task-specific grasping. GATER is trained on the new dataset and achieve task-specific grasping inference with 94.6% success rate. Finally, the effectiveness of GATER algorithm is tested on a real service robot platform. GATER algorithm has its potential in human behavior prediction and human-robot interaction.