Effect of shared attention on human robot communication
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Effect of Shared-attention on Human-Robot Communication. Written by Junyi Yamato, Kazuhiko Shinozawa, Futoshi Naya Presentation by Bert Gao. Introduction. Agents and Robots are being developed that can serve as human communication partners.

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Effect of Shared-attention on Human-Robot Communication

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Effect of shared attention on human robot communication

Effect of Shared-attention on Human-Robot Communication

Written by

Junyi Yamato, Kazuhiko Shinozawa, Futoshi Naya

Presentation by

Bert Gao


Introduction

Introduction

  • Agents and Robots are being developed that can serve as human communication partners.

  • Similarity and Difference between Agents and Robots.

  • Experiment condition: robot has to share the same space with users in order to perform as a good persuader.

  • Aim: Measure the effect of shared-attention in human-robot communication by the experiment.


Introduction cont

Introduction (cont.)


Experiment

Experiment

  • Color sample plate, color names

    • Subjects looked at the color sample plate, and were asked to choose the color name from two candidates.

    • All colors in the task were ambiguous, and some names were not so familiar to ordinary people. (e.g. carmine or vermilion for a bright red color plate)

    • The answer was not obvious and most subjects had no prior references.

    • Candidates of color names were chosen as the rule that expected average matching ratio would become around 0.5 with no-recommendation condition.

    • Same recommended candidates for all subjects.


Experiment cont

Experiment (cont.)

  • Subjects

    • 28 people (14 male and 14 female)

    • Age from 21 to 29, average 24.0 and the standard deviation was 1.83

    • Each subject saw 30 color plates in total, which were in the same order for all subjects.

  • TEG (Tokyo University Egogram)

    • Subjects were required to take a personality-profiling test, TEG.

    • TEG consists of 60 questions and measures five personality factors: CP (critical parent), NP (nurturing parent), A (adult), FC (free child), and AC (adapted child). Each factor is scaled from 0 to 20.

  • Achievement of shared-attention

    • Gaze direction of robot: subject, color plate, button box, and other.

    • Gaze direction of subject: robot face, robot (other), color plate, button box, and other.

    • When both robot and subject looked at the same direction, shared-attention was considered to be achieved.


Experiment cont1

Experiment (cont.)

  • Robot

    • Head robot with a human face tracking feature, built by the MIT AI Laboratory, named Kismet.

    • Tow eyes with video camera in each, eyelids, a mouth with expressive lips, two fan-like ears, and a moveable neck.

    • Vision system can extract and track the skin color region so that eye contact with the subject can be established.

    • Speech was generated by the “Fluet” Japanese speech synthesizer.


Result

Result

  • Matching ratio: 0.57, higher than that in non-recommendation condition, but not statistically significant different.

  • No correlation between the matching ratio and the amount of shared-attention time for all subjects.

  • A high-AC (adapted child) group of subjects (AC factor was more than 11) demonstrated a strong correlation between the matching ratio and SA time (Spearman’s r=0.51, p=0.051).

  • Comparing high-SA group and low-SA group among high-AC subjects, the matching ratio of the former was higher, and difference was statistically significant by t-test (p<0.05).


Discussion

Discussion

  • Shared-attention is important for human-robot communication.

  • Shared-attention had not only positive affects but also negative affects.

  • Robot can pretend to follow a user’s gaze by using an information source other than actual eye direction in real time.


Conclusion

Conclusion

  • Amount of time that shared-attention was achieved has a positive correlation with the strength of the effect on human decision-making.


Evaluation

Evaluation

  • Evaluation

    • Cons

      • Results are limited to a specific type of subjects (a high-AC factor group)

      • Subjects limitation: age 21-29, average age 24.0

      • Shared-attention was based on face direction other than real eye direction

    • Pros

      • Use TEG (Tokyo University Egogram) to categorize the subjects.

      • Pseudo shared-attention, robot can pretend to follow a user’s gaze by using an information source other than actual eye direction in real time, reduces the image acquiring and processing.


Future work

Future work

  • Eye direction catching combined with face direction catching would be more accurate to measure shared-attention.

  • Used into designing of robot communication partner (instructor, assistant, consultant, and etc.). Improve the communication between human and robots.


Kismet

Kismet

  • Video: Vocal turn taking

  • Kismet homepage in MIT

    http://www.ai.mit.edu/projects/humanoid-robotics-group/kismet/kismet.html


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