| Autor: |
Skoviera, Radoslav, Stepanova, Karla, Tesar, Michael, Sejnova, Gabriela, Sedlar, Jiri, Vavrecka, Michal, Babuska, Robert, Sivic, Josef |
| Rok vydání: |
2019 |
| Předmět: |
|
| Zdroj: |
The IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS) 2018, Workshop on: Towards Intelligent Social Robots: From Naive Robots to Robot Sapiens http://intelligent-social-robots-ws.com/materials/ |
| Druh dokumentu: |
Working Paper |
| Popis: |
In this paper, we consider the problem of learning object manipulation tasks from human demonstration using RGB or RGB-D cameras. We highlight the key challenges in capturing sufficiently good data with no tracking devices - starting from sensor selection and accurate 6DoF pose estimation to natural language processing. In particular, we focus on two showcases: gluing task with a glue gun and simple block-stacking with variable blocks. Furthermore, we discuss how a linguistic description of the task could help to improve the accuracy of task description. We also present the whole architecture of our transfer of the imitated task to the simulated and real robot environment. |
| Databáze: |
arXiv |
| Externí odkaz: |
|
