Dynamic communication of humanoid robot with multiple people based on interaction distance

Tasaki, Tomohiro; Matsumoto, Shinsuke; Ohba, Hayato; Toda, Mikito; Komatani, Kazunori; Ogata, Tetsuya; Okuno, Hiroshi

doi:10.1109/roman.2004.1374732

Cited by 22 publications

(6 citation statements)

References 3 publications

(2 reference statements)

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“…(Depending on the nature of the interaction, i.e. if it is between two friends or a more formal interaction, different zones are used [11] [16]). Thus the robot should at least be able to identify humans within a circle with radius of 3.5 meters, but since it should be able to discover people before the core interaction begins, an even larger area should be covered.…”

Section: Identification and Feature Extractionmentioning

confidence: 99%

“…When talking about HRI the first things that probably comes to mind is a robot talking to a person or a group of people. Much research has been and is being done concerning these rather explicit interactions, for instance [13], [16] and [17]. However succeeding with much more subtile interactions, that might happen on a person's unconscious level, is very important to get a good impression of the robot.…”

Section: Related Workmentioning

confidence: 99%

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Modeling of natural human-robot encounters

Bergström

Kanda²,

Miyashita³

et al. 2008

2008 IEEE/RSJ International Conference on Intelligent Robots and Systems

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For a person to feel comfortable when interacting with a robot, it is necessary for it to behave in an expected way. This should of course be the case during the actual interaction, but is in one sense even more important during the time preceding it. If someone is uncertain of what to expect from a robot when looking at it from a distance and approaching it, the robot's behavior can make the difference between that person choosing to interact or not. People's behaviors around a robot not reacting to them were observed during a field trial. Based on those observations people were classified into four groups depending on their estimated interest in interacting with the robot. People were tracked with a laser range finder based system, and their position, direction of motion and speed were estimated. A second classification based on that information was made. The two classifications were then mapped to each other. Different actions were then created for the robot to be able to react naturally to different human behaviors. In this thesis three different robot behaviors in a crowded environment are evaluated with respect to how natural they appear. With one behavior the robot actively tried to engage people, with one it passively indicated that people had been noticed and with the third behavior it made random gestures. During an experiment, test subjects were instructed to act according to the groups from the classification based on interest, and the robot's performance was evaluated with regard to how natural it appeared. Both first-and third-person evaluations made clear that the active and passive behavior were considered equally natural, while a robot randomly making gestures was considered much less natural.

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Section: Identification and Feature Extractionmentioning

confidence: 99%

Section: Related Workmentioning

confidence: 99%

Modeling of natural human-robot encounters

Bergström

Kanda²,

Miyashita³

et al. 2008

2008 IEEE/RSJ International Conference on Intelligent Robots and Systems

View full text Add to dashboard Cite

show abstract

“…Tasaki et al . developed a robot that chooses a target person based on people's distance . Gockley et al .…”

Section: Related Workmentioning

confidence: 99%

Social interaction with visitors: mobile guide robots capable of offering a museum tour

Yousuf

Kobayashi

Kuno

et al. 2019

IEEJ Transactions Elec Engng

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The purpose of this study is to develop a mobile museum guide (MG) robot capable of creating and controlling spatial formations with visitors in different situations. Although much research has already been conducted in the area of nonverbal communication between guide robots and humans, the creation and controlling of spatial formations with multiple visitors is a fundamental function for MG robots that remains unexplored. Drawing upon psychological and sociological studies on the spatial relationships between humans, it is evident that to be effective MG robots should also possess the capability to create and control spatial formations in various situations. A MG robot needs to establish a spatial formation to initiate interaction with the visitors; a spatial formation is a prerequisite before the robot can begin explaining an exhibit. Moreover, the guide robot must be able to identify interested bystanders and invite them into an ongoing explanation session, necessitating a reconfiguring of the spatial formation. Finally, the robot must be able to do this while continuing to explain multiple exhibits in a cohesive fashion. To devise a system capable of meeting these needs, we began by observing and videotaping scenes of actual museum galleries. Based on analyzing these data, we found that MG creates spatial formation with the visitors in a systematic way. We then developed a mobile robot system able to create and control spatial formations while guiding multiple visitors. A particle filter framework is employed to track the visitors' positions and body orientations and the orientations of their heads. We then evaluated the guide robot system in a series of experiments that focused on different situations where a guide robot creates a spatial formation with visitors. © 2019 Institute of Electrical Engineers of Japan. Published by John Wiley & Sons, Inc.

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“…Psycholinguistic studies revealed that speaking status plays an important role in attention, in that a listener’s visual attention is driven by what they hear [35,36]. As a result, the speaking status is usually considered as a fundamental feature of a robot’s attention system [37,38,39,40,41,42]. Robot attention models can be categorized into two groups: those which rely on fixed rules and those which rely on arithmetic equations.…”

Section: Related Workmentioning

confidence: 99%

“…A parameter of intimacy to determine the selection priority of an interactive partner using interaction distance was also proposed [41]. This proposed method is based on the concept of proxemics for communication between a robot and multiple people.…”

Section: Related Workmentioning

confidence: 99%

Vision-Based Attentiveness Determination Using Scalable HMM Based on Relevance Theory

Tiawongsombat

Jeong

Pirayawaraporn

et al. 2019

Sensors

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Attention capability is an essential component of human–robot interaction. Several robot attention models have been proposed which aim to enable a robot to identify the attentiveness of the humans with which it communicates and gives them its attention accordingly. However, previous proposed models are often susceptible to noisy observations and result in the robot’s frequent and undesired shifts in attention. Furthermore, most approaches have difficulty adapting to change in the number of participants. To address these limitations, a novel attentiveness determination algorithm is proposed for determining the most attentive person, as well as prioritizing people based on attentiveness. The proposed algorithm, which is based on relevance theory, is named the Scalable Hidden Markov Model (Scalable HMM). The Scalable HMM allows effective computation and contributes an adaptation approach for human attentiveness; unlike conventional HMMs, Scalable HMM has a scalable number of states and observations and online adaptability for state transition probabilities, in terms of changes in the current number of states, i.e., the number of participants in a robot’s view. The proposed approach was successfully tested on image sequences (7567 frames) of individuals exhibiting a variety of actions (speaking, walking, turning head, and entering or leaving a robot’s view). From these experimental results, Scalable HMM showed a detection rate of 76% in determining the most attentive person and over 75% in prioritizing people’s attention with variation in the number of participants. Compared to recent attention approaches, Scalable HMM’s performance in people attention prioritization presents an approximately 20% improvement.

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Dynamic communication of humanoid robot with multiple people based on interaction distance

Cited by 22 publications

References 3 publications

Modeling of natural human-robot encounters

Modeling of natural human-robot encounters

Social interaction with visitors: mobile guide robots capable of offering a museum tour

Vision-Based Attentiveness Determination Using Scalable HMM Based on Relevance Theory

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