Multi-human spatial social pattern understanding for a multi-modal robot through nonverbal social signals

Tseng, Shih-Huan; Hsu, Yuan-Han; Chiang, Yi-Shiu; Wu, Tung-Yen; Fu, Li‐Chen

doi:10.1109/roman.2014.6926307

Cited by 7 publications

(3 citation statements)

References 15 publications

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“…The goal of this work is to implement an engagement classifier for a robot teacher. Tseng et al ( 2014 ) present a robot that automatically recognizes the spatial patterns of human groups by analysing their non-verbal social signals in order to appropriately approach the group and offer services.…”

Section: Introductionmentioning

confidence: 99%

Systematic analysis of video data from different human–robot interaction studies: a categorization of social signals during error situations

et al. 2015

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Human–robot interactions are often affected by error situations that are caused by either the robot or the human. Therefore, robots would profit from the ability to recognize when error situations occur. We investigated the verbal and non-verbal social signals that humans show when error situations occur in human–robot interaction experiments. For that, we analyzed 201 videos of five human–robot interaction user studies with varying tasks from four independent projects. The analysis shows that there are two types of error situations: social norm violations and technical failures. Social norm violations are situations in which the robot does not adhere to the underlying social script of the interaction. Technical failures are caused by technical shortcomings of the robot. The results of the video analysis show that the study participants use many head movements and very few gestures, but they often smile, when in an error situation with the robot. Another result is that the participants sometimes stop moving at the beginning of error situations. We also found that the participants talked more in the case of social norm violations and less during technical failures. Finally, the participants use fewer non-verbal social signals (for example smiling, nodding, and head shaking), when they are interacting with the robot alone and no experimenter or other human is present. The results suggest that participants do not see the robot as a social interaction partner with comparable communication skills. Our findings have implications for builders and evaluators of human–robot interaction systems. The builders need to consider including modules for recognition and classification of head movements to the robot input channels. The evaluators need to make sure that the presence of an experimenter does not skew the results of their user studies.

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Section: Introductionmentioning

confidence: 99%

Systematic analysis of video data from different human–robot interaction studies: a categorization of social signals during error situations

et al. 2015

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“…There exists little research on supervisory control in multihuman-multi-robot systems. Several papers study the case in which humans play the role of a bystanders in, e.g., navigation of robots in a shared environment [38], [39], [40], [41], [42], [43] and human tracking [44], [45], [45], [46], [47], [48]. Other works focus on how to coordinate teams of humans and robots [49], [50], [51], [52], [53], [54], [35].…”

Section: Introductionmentioning

confidence: 99%

Improving Human Performance Using Mixed Granularity of Control in Multi-Human Multi-Robot Interaction

Patel¹,

Pinciroli²

2019

Preprint

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Due to the potentially large number of units involved, the interaction with a multi-robot system is likely to exceed the limits of the span of apprehension of any individual human operator. In previous work, we studied how this issue can be tackled by interacting with the robots in two modalities -environment-oriented and robot-oriented. In this paper, we study how this concept can be applied to the case in which multiple human operators perform supervisory control on a multirobot system. While the presence of extra operators suggests that more complex tasks could be accomplished, little research exists on how this could be achieved efficiently. In particular, one challenge arises -the out-of-the-loop performance problem caused by a lack of engagement in the task, awareness of its state, and trust in the system and in the other operators. Through a user study involving 28 human operators and 8 real robots, we study how the concept of mixed granularity in multi-human multi-robot interaction affects user engagement, awareness, and trust while balancing the workload between multiple operators.

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“…Therefore, human-like expressiveness is important in not only robot-human interactions but also robot-robot interactions. In group communication, robots are necessary social behavior in order to communicate naturally [1]. In robot-robot communication, robots are able to communicate by telepathy.…”

Section: Introductionmentioning

confidence: 99%

Effectiveness of a Sympathy Expression Model for the Bystander Robot

Iguchi

Takenouchi

Tokumaru

2016

IJAE

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Abstract:In this study, we verify the effectiveness of a sympathy expression model for a bystander robot that we have proposed. Because the social nature of a robot is important for communicating with multiple people, we focus on a robot that is in a bystander position, which is unrelated to the communication between participants. In our proposed model, the bystander can express emotions cooperatively in a group by learning the expressiveness of others. To test this, we assume an interaction of three robots using a neural network-based emotion generation model. The bystander learns moral emotions on the basis of its emotions from the communication between a speaker and an addressee. We verify the effectiveness of the model using a psychological model to evaluate the bystander from the position of the speaker.

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Multi-human spatial social pattern understanding for a multi-modal robot through nonverbal social signals

Cited by 7 publications

References 15 publications

Systematic analysis of video data from different human–robot interaction studies: a categorization of social signals during error situations

Systematic analysis of video data from different human–robot interaction studies: a categorization of social signals during error situations

Improving Human Performance Using Mixed Granularity of Control in Multi-Human Multi-Robot Interaction

Effectiveness of a Sympathy Expression Model for the Bystander Robot

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