at Google ScholarTitle data
Awais, Muhammad ; Henrich, Dominik:
Online intention learning for human-robot interaction by scene observation.
In:
IEEE Workshop on Advanced Robotics and its Social Impacts. -
München
,
2012
. - pp. 13-18
DOI: https://doi.org/10.1109/ARSO.2012.6213391
Abstract in another language
Intention recognition plays a key role in the cooperation among the humans. An intention describes an action or sequence of actions to be performed for achieving the intended purpose. The cooperating humans learn each others intentions while cooperation. In this paper we propose three ways how a robot can learn the intention of the cooperating human. In the first case, the robot learns the human intention by mapping the known human intention given in terms of scene information to the observed action sequence. The actions are already known to the robot. In the second case, the robot is only given the human actions but the robot estimates the human intention in terms of the changes that occur in the scene due to the human actions. The robot learns the human intention by mapping the observed action sequence to the human intention. The human intention is estimated from the scene information. In the third case, only the scene information is used in order to learn the human intention mapping. The scene information is used to infer the human actions as well as the human intention.
Further data
| Item Type: | Article in a book |
|---|---|
| Refereed: | Yes |
| Keywords: | Humans; Robots; Silicon; Hidden Markov models; Testing; Indexes; Collaboration |
| Institutions of the University: | Faculties > Faculty of Mathematics, Physics und Computer Science > Department of Computer Science > Chair Applied Computer Science III > Chair Applied Computer Science III - Univ.-Prof. Dr. Dominik Henrich |
| Result of work at the UBT: | Yes |
| DDC Subjects: | 000 Computer Science, information, general works > 004 Computer science |
| Date Deposited: | 20 Feb 2025 12:19 |
| Last Modified: | 20 Feb 2025 12:19 |
| URI: | https://eref.uni-bayreuth.de/id/eprint/92494 |