GATER: 把持・動作・対象の埋め込みと関係を学習するタスク特化型把持
GATER: Learning Grasp-Action-Target Embeddings and Relations for Task-Specific Grasping
把持道具・動作・対象物体の三つ組でタスクを定義し、埋め込み空間でそれらの関係を学習することで、タスクに応じた把持位置を推論する手法GATERを提案した論文。
分類: cs.RO
原文アブストラクト
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.
