Three-layered draw-attention model for humanoid robots with gestures and verbal cues

Osamu Sugiyama, Takayuki Kanda, Michita Imai, Hiroshi Ishiguro, Norihiro Hagita

Research output: Chapter in Book/Report/Conference proceedingConference contribution

24 Citations (Scopus)

Abstract

When we talk about objects in an environment, we indicate to a listener which object is currently under consideration by using pointing gesture and such reference terms as "this" and "that". Such reference terms play an important role in human interaction by quickly informing the listener of an indicated object's location. In this research, we propose a three-layered draw-attention model for humanoid robots with gestures and verbal cues. Our proposed three-layered model consists of three sub models: Reference Term Model (RTM), Limit Distance Model (LDM) and Object Property Model (OPM). RTM decides an appropriate reference term using functions constructed by an analysis of human behavior. LDM decides whether to use the object's property with a reference term. OPM decides the appropriate property for indicating the object by comparing object properties with each other. We developed an attention drawing system in a communication robot named "Robovie" based on the three layered model. We confirmed its effectiveness through the experiments.

Original languageEnglish
Title of host publication2005 IEEE/RSJ International Conference on Intelligent Robots and Systems, IROS
Pages2140-2145
Number of pages6
DOIs
Publication statusPublished - 2005
Externally publishedYes
EventIEEE IRS/RSJ International Conference on Intelligent Robots and Systems, IROS 2005 - Edmonton, AB, Canada
Duration: 2005 Aug 22005 Aug 6

Other

OtherIEEE IRS/RSJ International Conference on Intelligent Robots and Systems, IROS 2005
CountryCanada
CityEdmonton, AB
Period05/8/205/8/6

Fingerprint

Robots
Communication
Experiments

Keywords

  • Human-robot interaction
  • Human-robot interface
  • Humanoid robot

ASJC Scopus subject areas

  • Artificial Intelligence
  • Computer Vision and Pattern Recognition
  • Human-Computer Interaction
  • Control and Systems Engineering

Cite this

Sugiyama, O., Kanda, T., Imai, M., Ishiguro, H., & Hagita, N. (2005). Three-layered draw-attention model for humanoid robots with gestures and verbal cues. In 2005 IEEE/RSJ International Conference on Intelligent Robots and Systems, IROS (pp. 2140-2145). [1545293] https://doi.org/10.1109/IROS.2005.1545293

Three-layered draw-attention model for humanoid robots with gestures and verbal cues. / Sugiyama, Osamu; Kanda, Takayuki; Imai, Michita; Ishiguro, Hiroshi; Hagita, Norihiro.

2005 IEEE/RSJ International Conference on Intelligent Robots and Systems, IROS. 2005. p. 2140-2145 1545293.

Research output: Chapter in Book/Report/Conference proceedingConference contribution

Sugiyama, O, Kanda, T, Imai, M, Ishiguro, H & Hagita, N 2005, Three-layered draw-attention model for humanoid robots with gestures and verbal cues. in 2005 IEEE/RSJ International Conference on Intelligent Robots and Systems, IROS., 1545293, pp. 2140-2145, IEEE IRS/RSJ International Conference on Intelligent Robots and Systems, IROS 2005, Edmonton, AB, Canada, 05/8/2. https://doi.org/10.1109/IROS.2005.1545293
Sugiyama O, Kanda T, Imai M, Ishiguro H, Hagita N. Three-layered draw-attention model for humanoid robots with gestures and verbal cues. In 2005 IEEE/RSJ International Conference on Intelligent Robots and Systems, IROS. 2005. p. 2140-2145. 1545293 https://doi.org/10.1109/IROS.2005.1545293
Sugiyama, Osamu ; Kanda, Takayuki ; Imai, Michita ; Ishiguro, Hiroshi ; Hagita, Norihiro. / Three-layered draw-attention model for humanoid robots with gestures and verbal cues. 2005 IEEE/RSJ International Conference on Intelligent Robots and Systems, IROS. 2005. pp. 2140-2145
@inproceedings{997653c8f2644d6b95757fe2dc8ba839,
title = "Three-layered draw-attention model for humanoid robots with gestures and verbal cues",
abstract = "When we talk about objects in an environment, we indicate to a listener which object is currently under consideration by using pointing gesture and such reference terms as {"}this{"} and {"}that{"}. Such reference terms play an important role in human interaction by quickly informing the listener of an indicated object's location. In this research, we propose a three-layered draw-attention model for humanoid robots with gestures and verbal cues. Our proposed three-layered model consists of three sub models: Reference Term Model (RTM), Limit Distance Model (LDM) and Object Property Model (OPM). RTM decides an appropriate reference term using functions constructed by an analysis of human behavior. LDM decides whether to use the object's property with a reference term. OPM decides the appropriate property for indicating the object by comparing object properties with each other. We developed an attention drawing system in a communication robot named {"}Robovie{"} based on the three layered model. We confirmed its effectiveness through the experiments.",
keywords = "Human-robot interaction, Human-robot interface, Humanoid robot",
author = "Osamu Sugiyama and Takayuki Kanda and Michita Imai and Hiroshi Ishiguro and Norihiro Hagita",
year = "2005",
doi = "10.1109/IROS.2005.1545293",
language = "English",
isbn = "0780389123",
pages = "2140--2145",
booktitle = "2005 IEEE/RSJ International Conference on Intelligent Robots and Systems, IROS",

}

TY - GEN

T1 - Three-layered draw-attention model for humanoid robots with gestures and verbal cues

AU - Sugiyama, Osamu

AU - Kanda, Takayuki

AU - Imai, Michita

AU - Ishiguro, Hiroshi

AU - Hagita, Norihiro

PY - 2005

Y1 - 2005

N2 - When we talk about objects in an environment, we indicate to a listener which object is currently under consideration by using pointing gesture and such reference terms as "this" and "that". Such reference terms play an important role in human interaction by quickly informing the listener of an indicated object's location. In this research, we propose a three-layered draw-attention model for humanoid robots with gestures and verbal cues. Our proposed three-layered model consists of three sub models: Reference Term Model (RTM), Limit Distance Model (LDM) and Object Property Model (OPM). RTM decides an appropriate reference term using functions constructed by an analysis of human behavior. LDM decides whether to use the object's property with a reference term. OPM decides the appropriate property for indicating the object by comparing object properties with each other. We developed an attention drawing system in a communication robot named "Robovie" based on the three layered model. We confirmed its effectiveness through the experiments.

AB - When we talk about objects in an environment, we indicate to a listener which object is currently under consideration by using pointing gesture and such reference terms as "this" and "that". Such reference terms play an important role in human interaction by quickly informing the listener of an indicated object's location. In this research, we propose a three-layered draw-attention model for humanoid robots with gestures and verbal cues. Our proposed three-layered model consists of three sub models: Reference Term Model (RTM), Limit Distance Model (LDM) and Object Property Model (OPM). RTM decides an appropriate reference term using functions constructed by an analysis of human behavior. LDM decides whether to use the object's property with a reference term. OPM decides the appropriate property for indicating the object by comparing object properties with each other. We developed an attention drawing system in a communication robot named "Robovie" based on the three layered model. We confirmed its effectiveness through the experiments.

KW - Human-robot interaction

KW - Human-robot interface

KW - Humanoid robot

UR - http://www.scopus.com/inward/record.url?scp=79957994614&partnerID=8YFLogxK

UR - http://www.scopus.com/inward/citedby.url?scp=79957994614&partnerID=8YFLogxK

U2 - 10.1109/IROS.2005.1545293

DO - 10.1109/IROS.2005.1545293

M3 - Conference contribution

SN - 0780389123

SN - 9780780389120

SP - 2140

EP - 2145

BT - 2005 IEEE/RSJ International Conference on Intelligent Robots and Systems, IROS

ER -