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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. 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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  1. Effect of Shared-attention on Human-Robot Communication Written by Junyi Yamato, Kazuhiko Shinozawa, Futoshi Naya Presentation by Bert Gao

  2. 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.

  3. Introduction (cont.)

  4. 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.

  5. 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.

  6. 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.

  7. 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).

  8. 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.

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

  10. 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.

  11. 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.

  12. 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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