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Effect of Shared-attention for Human-Robot Interaction

Effect of Shared-attention for Human-Robot Interaction. Junji Yamato jy@acm.org NTT Communication Science Labs., NTT Corp. Japan Kazuhiko Shinozawa, Futoshi Naya ATR Intelligent Robot and Communication Labs. Aim. To build Social Robot/Agent Sub goal To establish Evaluation methods

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Effect of Shared-attention for Human-Robot Interaction

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  1. Effect of Shared-attention for Human-Robot Interaction Junji Yamato jy@acm.org NTT Communication Science Labs., NTT Corp. Japan Kazuhiko Shinozawa, Futoshi Naya ATR Intelligent Robot and Communication Labs.

  2. Aim • To build Social Robot/Agent • Sub goal • To establish • Evaluation methods • Design guidelines for communication of human-robot/agent

  3. Method • To measure the influence of Agent/Robot on users • Acceptance ratio of agent/robot recommendation

  4. Color name selection task • No “correct” answer • Easy to be influenced Blue or Green? Cobalt green or emerald green? Skin color or KARE-IRO? SUMIRE-IRO or AYAME-IRO? ---- ---- Total:30 questions. (from color name text book)

  5. Four experiments • Compared agent and robot • Compared agent and robot in physical world • Measured the effect of eye contact • Measured the effect of shared-attention Detailed description of Experiment 1 and 2 Shinozawa, K., Naya, F., Yamato, J., and Kogure, K. Differences in Effect of Robot and Screen Agent Recommendations on Human Decision-Making , IJHCS (to appear) Experiment 1, 2, and description of K4(robot) Yamato, J., Shinozawa, K., Brooks, R., and Naya, F. Human-Robot Dynamic Social Interaction. NTT Technical Review 1, 6(2003), 37-43. Available on-line http://www.ntt.co.jp/tr/ Back number -> Sep. 2003

  6. Experiment 1：Compare Agent and Robot Agent Robot Agent Robot • Conditions: 30 questions, 30 subjects in each group ‐Same question sequences, same voice, similar gesture • Measurement: acceptance ratio, questionnaire

  7. Experiment 1: Robot

  8. Experiment 1: Result • Acceptance：agent > robot (p<.01) • Familiarity：independent

  9. Gap Gap Gap Initial expectation Robot has more influence because it lives in 3D world, same as subjects. agent ○ robot ×

  10. Experiment 2: Compare in physical world Color plate Button box Button box • No recommendation (30 subjects) • Recommendation by robot（31 subjects） • Recommendation by agent (30 subjects)

  11. Experiments

  12. Experiment 2: Result • selection ratio：robot > agent( p < 0.05) robot>> no recommendation ( p < 0.01)

  13. × ○ × ○ Embodiment and communication Experiment 1 and 2: Results Consistency matters. Physical world Media world agent robot

  14. Why robot is better? • Easy to detect gaze • Eye contact • Shared attention/joint attention Measure the effect of eye contact and shared-attention

  15. Experiment 3: Effect of eye contact (mutual gaze) • Eye contact was established by face tracking • Eye contact time: period that subject looked at robot and robot looked at subject • Eye contact time and selection ratio? • Two groups (14 subjects each) • Eye contact, and NO eye contact

  16. Robots

  17. Selection ratio • Higher selection ratio for eye contact group • K4: No E.C. < E.C.(p=0.012) • Rabbit: No E.C. < E.C. (p=0.003)

  18. Experiment 4: Effect of shared-attention • Shared attention: • Period that robot looks at an object and subject looks at the same object. (color plate, button box) • SA time and selection ratio • Is there correlation?

  19. Establishing shared-attention • Robot looks at color plate and button box by prepared program • Eye contact established by face tracking Example: video

  20. Experimental conditions • 28 subjects • SA time = 51.7 sec (total for 30 questions) • (Longer than in Experiment 3 ) • Selection ratio. Average: 0.57 S.D.= 0.14 • Some subjects were positive, and others were not. Clear contrast, from the questionnaire. Example: Robot is prompting wrong choice. I feel the robot forced me to select his recommendation (negative).

  21. SA time and selection ratio • No correlation Selection ratio Shared-Attention time (count) 50count=1sec.

  22. Clustering subjects by TEG(Ego-gram) • Ego-gram based on transactional analysis • Measure three ego-states by questionnaire • CP, NP (critical parent, nurturing parent) • A (adult) • FC, AC (free child, adapted child) • TEG (Tokyo Univ. Egogram）is common in Japan

  23. High/Low TEG measurement and SA time. • Strong correlation in SA time and acceptance ratio for high AC (Adapted Child) group

  24. SA time and selection ratio (high AC & low CP group) • Positive correlation(Speaman’s r=0.51,p=0.051).

  25. SA time and selection ratio • High-SA group = high selection ratio (p<0.05) （high AC group)

  26. Result and Discussion • High AC subject (obedient type) showed positive correlation between SA time and selection ratio. • No significant difference between SA time itself and selection ratios for high AC and low AC groups • Eye contact and shared-attention promote close communication. Some people like such intimate relation, but others don’t. It depends on the character. • SA is effective. Even SA was not “actually” realized. We do not need to develop image understanding technology; we just have to fake it.

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