Motion imitation and recognition using parametric hidden Markov models
Herzog, Dennis ; Ude, Aleš and Krüger, Volker LU
(2008)
2008 8th IEEE-RAS International Conference on Humanoid Robots, Humanoids 2008
p.339-346
- Abstract
The recognition and synthesis of parametric movements play an important role in human-robot interaction. To understand the whole purpose of an arm movement of a human agent, both its recognition (e.g., pointing or reaching) as well as its parameterization (i.e., where the agent is pointing at) are important. Only together they convey the whole meaning of an action. Similarly, to imitate a movement, the robot needs to select the proper action and parameterize it, e. g., by the relative position of the object that needs to be grasped. We propose to utilize parametric hidden Markov models (PH-MMs), which extend the classical HMMs by introducing a joint parameterization of the observation densities, to simultaneously solve the problems of... (More)
The recognition and synthesis of parametric movements play an important role in human-robot interaction. To understand the whole purpose of an arm movement of a human agent, both its recognition (e.g., pointing or reaching) as well as its parameterization (i.e., where the agent is pointing at) are important. Only together they convey the whole meaning of an action. Similarly, to imitate a movement, the robot needs to select the proper action and parameterize it, e. g., by the relative position of the object that needs to be grasped. We propose to utilize parametric hidden Markov models (PH-MMs), which extend the classical HMMs by introducing a joint parameterization of the observation densities, to simultaneously solve the problems of action recognition, parameterization of the observed actions, and action synthesis. The proposed approach was fully implemented on a humanoid robot HOAP-3. To evaluate the approach, we focused on reaching and pointing actions. Even though the movements are very similar in appearance, our approach is able to distinguish the two movement types and discover the parameterization, and is thus enabling both, action recognition and action synthesis. Through parameterization we ensure that the synthesized movements can be applied to different configurations of the external world and are thus suitable for actions that involve the manipulation of objects.
(Less)
- author
- Herzog, Dennis
; Ude, Aleš
and Krüger, Volker
LU
- publishing date
- 2008-12-01
- type
- Chapter in Book/Report/Conference proceeding
- publication status
- published
- subject
- host publication
- 2008 8th IEEE-RAS International Conference on Humanoid Robots, Humanoids 2008
- article number
- 4756002
- pages
- 8 pages
- publisher
- IEEE - Institute of Electrical and Electronics Engineers Inc.
- conference name
- 2008 8th IEEE-RAS International Conference on Humanoid Robots, Humanoids 2008
- conference location
- Daejeon, Korea, Republic of
- conference dates
- 2008-12-01 - 2008-12-03
- external identifiers
-
- scopus:63549139567
- ISBN
- 978-1-4244-2821-2
- DOI
- 10.1109/ICHR.2008.4756002
- language
- English
- LU publication?
- no
- id
- 090fdae2-3e98-4d6f-bee8-a80e2bec17db
- date added to LUP
- 2019-06-28 09:25:36
- date last changed
- 2022-01-31 22:50:20
@inproceedings{090fdae2-3e98-4d6f-bee8-a80e2bec17db,
abstract = {{<p>The recognition and synthesis of parametric movements play an important role in human-robot interaction. To understand the whole purpose of an arm movement of a human agent, both its recognition (e.g., pointing or reaching) as well as its parameterization (i.e., where the agent is pointing at) are important. Only together they convey the whole meaning of an action. Similarly, to imitate a movement, the robot needs to select the proper action and parameterize it, e. g., by the relative position of the object that needs to be grasped. We propose to utilize parametric hidden Markov models (PH-MMs), which extend the classical HMMs by introducing a joint parameterization of the observation densities, to simultaneously solve the problems of action recognition, parameterization of the observed actions, and action synthesis. The proposed approach was fully implemented on a humanoid robot HOAP-3. To evaluate the approach, we focused on reaching and pointing actions. Even though the movements are very similar in appearance, our approach is able to distinguish the two movement types and discover the parameterization, and is thus enabling both, action recognition and action synthesis. Through parameterization we ensure that the synthesized movements can be applied to different configurations of the external world and are thus suitable for actions that involve the manipulation of objects.</p>}},
author = {{Herzog, Dennis and Ude, Aleš and Krüger, Volker}},
booktitle = {{2008 8th IEEE-RAS International Conference on Humanoid Robots, Humanoids 2008}},
isbn = {{978-1-4244-2821-2}},
language = {{eng}},
month = {{12}},
pages = {{339--346}},
publisher = {{IEEE - Institute of Electrical and Electronics Engineers Inc.}},
title = {{Motion imitation and recognition using parametric hidden Markov models}},
url = {{http://dx.doi.org/10.1109/ICHR.2008.4756002}},
doi = {{10.1109/ICHR.2008.4756002}},
year = {{2008}},
}