Primitive-based action representation and recognition
Sanmohan ; Krüger, Volker LU
; Kragic, Danica
and Kjellström, Hedvig
(2011)
In Advanced Robotics
25(6-7).
p.871-891
- Abstract
In robotics, there has been a growing interest in expressing actions as a combination of meaningful subparts commonly called motion primitives. Primitives are analogous to words in a language. Similar to words put together according to the rules of language in a sentence, primitives arranged with certain rules make an action. In this paper we investigate modeling and recognition of arm manipulation actions at different levels of complexity using primitives. Primitives are detected automatically in a sequential manner. Here, we assume no prior knowledge on primitives, but look for correlating segments across various sequences. All actions are then modeled within a single hidden Markov models whose structure is learned incrementally as... (More)
In robotics, there has been a growing interest in expressing actions as a combination of meaningful subparts commonly called motion primitives. Primitives are analogous to words in a language. Similar to words put together according to the rules of language in a sentence, primitives arranged with certain rules make an action. In this paper we investigate modeling and recognition of arm manipulation actions at different levels of complexity using primitives. Primitives are detected automatically in a sequential manner. Here, we assume no prior knowledge on primitives, but look for correlating segments across various sequences. All actions are then modeled within a single hidden Markov models whose structure is learned incrementally as new data is observed. We also generate an action grammar based on these primitives and thus link signals to symbols.
(Less)
- author
- Sanmohan
; Krüger, Volker
LU
; Kragic, Danica
and Kjellström, Hedvig
- publishing date
- 2011-04-20
- type
- Contribution to journal
- publication status
- published
- subject
- keywords
- activity modeling, high-level event, imitation learning, Primitive detection
- in
- Advanced Robotics
- volume
- 25
- issue
- 6-7
- pages
- 21 pages
- publisher
- Taylor & Francis
- external identifiers
-
- scopus:79954472581
- ISSN
- 0169-1864
- DOI
- 10.1163/016918611X563346
- language
- English
- LU publication?
- no
- id
- d822a06d-789b-4e3f-9727-050715974669
- date added to LUP
- 2019-06-28 09:22:36
- date last changed
- 2022-01-31 22:45:08
@article{d822a06d-789b-4e3f-9727-050715974669,
abstract = {{<p>In robotics, there has been a growing interest in expressing actions as a combination of meaningful subparts commonly called motion primitives. Primitives are analogous to words in a language. Similar to words put together according to the rules of language in a sentence, primitives arranged with certain rules make an action. In this paper we investigate modeling and recognition of arm manipulation actions at different levels of complexity using primitives. Primitives are detected automatically in a sequential manner. Here, we assume no prior knowledge on primitives, but look for correlating segments across various sequences. All actions are then modeled within a single hidden Markov models whose structure is learned incrementally as new data is observed. We also generate an action grammar based on these primitives and thus link signals to symbols.</p>}},
author = {{Sanmohan and Krüger, Volker and Kragic, Danica and Kjellström, Hedvig}},
issn = {{0169-1864}},
keywords = {{activity modeling; high-level event; imitation learning; Primitive detection}},
language = {{eng}},
month = {{04}},
number = {{6-7}},
pages = {{871--891}},
publisher = {{Taylor & Francis}},
series = {{Advanced Robotics}},
title = {{Primitive-based action representation and recognition}},
url = {{http://dx.doi.org/10.1163/016918611X563346}},
doi = {{10.1163/016918611X563346}},
volume = {{25}},
year = {{2011}},
}