Name
Abstract | ||
|---|---|---|
This paper presents semantic-based methods for the understanding of human movements in robotic applications. To understand human movements, robots need to first, recognize the observed or demonstrated human activities, and secondly, learn different parameters to execute an action or robot behavior. In order to achieve that, several challenges need to be addressed such as the automatic segmentation of human activities, identification of important features of actions, determine the correct sequencing between activities, and obtain the correct mapping between the continuous data and the symbolic and semantic interpretations of the human movements. This paper aims to present state-of-the-art semantic-based approaches, especially the new emerging approaches that tackle the challenges of finding generic and compact semantic models for the robotics domain. Finally, we will highlight potential breakthroughs and challenges for the next years such as achieving scalability, better generalization, compact and flexible models, and higher system accuracy. |
Year | DOI | Venue |
|---|---|---|
2019 | 10.1016/j.robot.2019.05.013 | Robotics and Autonomous Systems |
Keywords | Field | DocType |
Semantic representations,Understanding human movements,Human activity recognition,Robot action execution,Intelligent systems | The Symbolic,Computer vision,Computer science,Segmentation,Human–computer interaction,Artificial intelligence,Behavior-based robotics,Robot,Robotics,Scalability | Journal |
Volume | ISSN | Citations |
119 | 0921-8890 | 0 |
PageRank | References | Authors |
0.34 | 0 | 3 |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Karinne Ramirez-Amaro | 1 | 36 | 5.19 |
| Yezhou Yang | 2 | 355 | 38.60 |
| Gordon Cheng | 3 | 1250 | 115.33 |