  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.
