Human-Robot Interaction Learning Using Demonstration-Based Learning and -Learning in a Pervasive Sensing Environment
Given that robots provide services in any locations after they move toward humans, the pervasive sensing environment can provide diverse kinds of services through the robots not depending on the locations of humans. For various services, robots need to learn accurate motor primitives such as walking...
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| Main Authors: | , , , , , |
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| Format: | Article |
| Language: | English |
| Published: |
Wiley
2013-11-01
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| Series: | International Journal of Distributed Sensor Networks |
| Online Access: | https://doi.org/10.1155/2013/782043 |
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| _version_ | 1832555252723744768 |
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| author | Yunsick Sung Seoungjae Cho Kyhyun Um Young-Sik Jeong Simon Fong Kyungeun Cho |
| author_facet | Yunsick Sung Seoungjae Cho Kyhyun Um Young-Sik Jeong Simon Fong Kyungeun Cho |
| author_sort | Yunsick Sung |
| collection | DOAJ |
| description | Given that robots provide services in any locations after they move toward humans, the pervasive sensing environment can provide diverse kinds of services through the robots not depending on the locations of humans. For various services, robots need to learn accurate motor primitives such as walking and grabbing objects. However, learning motor primitives in a pervasive sensing environment are very time consuming. Several previous studies have considered robots learning motor primitives and interacting with humans in virtual environments. Given that a robot learns motor primitives based on observations, a disadvantage is that there is no way of defining motor primitives that cannot be observed by a robot. In this paper, we develop a novel interaction learning approach based on a virtual environment. The motor primitives are defined by manipulating a robot directly using demonstration-based learning. In addition, a robot can apply Q -learning to learn interactions with humans. In an experiment, using the proposed method, the motor primitives were generated intuitively and the amount of movement required by a virtual human in one of the experiments was reduced by about 25% after applying the generated motor primitives. |
| format | Article |
| id | doaj-art-0754409751824c3a955f01b2a6b0d879 |
| institution | Kabale University |
| issn | 1550-1477 |
| language | English |
| publishDate | 2013-11-01 |
| publisher | Wiley |
| record_format | Article |
| series | International Journal of Distributed Sensor Networks |
| spelling | doaj-art-0754409751824c3a955f01b2a6b0d8792025-02-03T05:48:37ZengWileyInternational Journal of Distributed Sensor Networks1550-14772013-11-01910.1155/2013/782043782043Human-Robot Interaction Learning Using Demonstration-Based Learning and -Learning in a Pervasive Sensing EnvironmentYunsick Sung0Seoungjae Cho1Kyhyun Um2Young-Sik Jeong3Simon Fong4Kyungeun Cho5 The Department of Game Mobile Contents, Keimyung University, Daegu 704-701, Republic of Korea Department of Multimedia Engineering, Graduate School of Dongguk University, Seoul 100-715, Republic of Korea Department of Multimedia Engineering, Dongguk University, Seoul 100-715, Republic of Korea Department of Multimedia Engineering, Dongguk University, Seoul 100-715, Republic of Korea Department of Computer and Information Science, University of Macau, Macau 3000, China Department of Multimedia Engineering, Dongguk University, Seoul 100-715, Republic of KoreaGiven that robots provide services in any locations after they move toward humans, the pervasive sensing environment can provide diverse kinds of services through the robots not depending on the locations of humans. For various services, robots need to learn accurate motor primitives such as walking and grabbing objects. However, learning motor primitives in a pervasive sensing environment are very time consuming. Several previous studies have considered robots learning motor primitives and interacting with humans in virtual environments. Given that a robot learns motor primitives based on observations, a disadvantage is that there is no way of defining motor primitives that cannot be observed by a robot. In this paper, we develop a novel interaction learning approach based on a virtual environment. The motor primitives are defined by manipulating a robot directly using demonstration-based learning. In addition, a robot can apply Q -learning to learn interactions with humans. In an experiment, using the proposed method, the motor primitives were generated intuitively and the amount of movement required by a virtual human in one of the experiments was reduced by about 25% after applying the generated motor primitives.https://doi.org/10.1155/2013/782043 |
| spellingShingle | Yunsick Sung Seoungjae Cho Kyhyun Um Young-Sik Jeong Simon Fong Kyungeun Cho Human-Robot Interaction Learning Using Demonstration-Based Learning and -Learning in a Pervasive Sensing Environment International Journal of Distributed Sensor Networks |
| title | Human-Robot Interaction Learning Using Demonstration-Based Learning and -Learning in a Pervasive Sensing Environment |
| title_full | Human-Robot Interaction Learning Using Demonstration-Based Learning and -Learning in a Pervasive Sensing Environment |
| title_fullStr | Human-Robot Interaction Learning Using Demonstration-Based Learning and -Learning in a Pervasive Sensing Environment |
| title_full_unstemmed | Human-Robot Interaction Learning Using Demonstration-Based Learning and -Learning in a Pervasive Sensing Environment |
| title_short | Human-Robot Interaction Learning Using Demonstration-Based Learning and -Learning in a Pervasive Sensing Environment |
| title_sort | human robot interaction learning using demonstration based learning and learning in a pervasive sensing environment |
| url | https://doi.org/10.1155/2013/782043 |
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