Abstract
The ability to learn is essential for robots if they are to function within human environments. Learning requires an understanding of the underlying structure of what has been observed. This paper proposes a learning method that automatically creates Petri nets from observation of human demonstrations to model the underlying structure of tasks. The Petri net can be learned via a single or multiple demonstrations. The learned Petri nets are capable of generating action sequences to allow a robot to imitate the task. The proposed model also allows for generalization and variations in performing the task. The proposed method is tested on demonstrations of block stacking tasks and verified through robot imitation of the tasks in simulation and in physical experiments.
Original language | English |
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Title of host publication | 22nd IEEE International Symposium on Robot and Human Interactive Communication |
Subtitle of host publication | "Living Together, Enjoying Together, and Working Together with Robots!", IEEE RO-MAN 2013 |
Publisher | IEEE, Institute of Electrical and Electronics Engineers |
Pages | 31-36 |
Number of pages | 6 |
ISBN (Print) | 9781479905072 |
DOIs | |
Publication status | Published - 11 Dec 2013 |
Externally published | Yes |
Event | IEEE/RSJ International Symposium on Robot and Human Interactive Communication 2013 - Gyeongju, Korea, South Duration: 26 Aug 2013 → 29 Aug 2013 Conference number: 22nd https://ieeexplore.ieee.org/xpl/conhome/6604421/proceeding (Proceedings) |
Publication series
Name | Proceedings - IEEE International Workshop on Robot and Human Interactive Communication |
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Conference
Conference | IEEE/RSJ International Symposium on Robot and Human Interactive Communication 2013 |
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Abbreviated title | RO-MAN 2013 |
Country/Territory | Korea, South |
City | Gyeongju |
Period | 26/08/13 → 29/08/13 |
Internet address |