2021/11/27 by Ming Sun, Yue Gao, Sun, Ming +1 · 1 citation
Computer Science · Engineering · #FOS: Computer and information sciences #Human Pose and Action Recognition #Multimodal Machine Learning Applications #Robot Manipulation and Learning #Robotics (cs.RO) #cs.RO
paper · pdf · doi:10.48550/arxiv.2111.13815
arxiv created 2021/11/27 · openalex publication_date 2021/11/27 · arxiv updated 2021/11/30 · openalex created_date 2025/10/10 · openalex updated_date 2026/07/28
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.