This work documents a playful human-robot interaction, in the form of a game of charades, through which a humanoid robot is able to learn how to produce and recognize gestures by interacting with human participants. We describe an extensive dataset of gesture recordings, which can be used for future research into gestures, specifically for human-robot interaction applications.

References

[1]

P. Bremner, A. G. Pipe, C. Melhuish, M. Fraser, and S. Subramanian, "The effects of robot-performed co-verbal gesture on listener behaviour," in Proceedings of the 11th IEEE-RAS International Conference on Humanoid Robots (Humanoids). IEEE, 2011, pp. 458--465.

Google Scholar

[2]

J. de Wit, T. Schodde, B. Willemsen, K. Bergmann, M. de Haas, S. Kopp, E. Krahmer, and P. Vogt, "The effect of a robot's gestures and adaptive tutoring on children's acquisition of second language vocabularies," in Proceedings of the 2018 ACM/IEEE International Conference on Human-Robot Interaction. ACM, 2018, pp. 50--58.

Google Scholar

[3]

D. McNeill, "So you think gestures are nonverbal?" Psychological Review, vol. 92, no. 3, pp. 350--371, 1985.

Crossref

Google Scholar

[4]

B. D. Argall, S. Chernova, M. Veloso, and B. Browning, "A survey of robot learning from demonstration," Robotics and autonomous systems, vol. 57, no. 5, pp. 469--483, 2009.

Google Scholar

[5]

M. E. Cabrera and J. P. Wachs, "A human-centered approach to one-shot gesture learning," Frontiers in Robotics and AI, vol. 4, p. 8, 2017.

Crossref

Google Scholar

Playing charades with a robot: collecting a large dataset of human gestures through HRI
1. Computer systems organization
  1. Embedded and cyber-physical systems

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Published In

HRI '19: Proceedings of the 14th ACM/IEEE International Conference on Human-Robot Interaction

March 2019

812 pages

ISBN:9781538685556

General Chairs:
Jung Kim
KAIST, Korea
,
Adriana Tapus
ENSTA ParisTech, France
,
Program Chairs:
David Sirkin
Stanford University
,
Malte Jung
Cornell University
,
Sonya S. Kwak
KIST, Korea

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IEEE-RAS: Robotics and Automation

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IEEE Press

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Published: 10 January 2020

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HRI '19

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HRI '19: ACM/IEEE International Conference on Human-Robot Interaction

March 11 - 14, 2019

Daegu, Republic of Korea

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Overall Acceptance Rate 268 of 1,124 submissions, 24%

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