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Gaze Control of Humanoid Robot for Learning from Demonstration

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Robot Intelligence Technology and Applications 4

Part of the book series: Advances in Intelligent Systems and Computing ((AISC,volume 447))

Abstract

Robots can learn knowledge by observing demonstration of humans. As tutees, robots need to not only observe human behaviors, but also make proper feedbacks for human tutors because learning is an interactive process in which information is delivered in bidirectional ways between humans and robots. Gaze is an adequate method for robots to provide human tutors with feedbacks that robots are concentrating on current learning because gaze directly represents where they are paying attention to. This paper proposes a gaze control algorithm with a state machine in learning from demonstration. A human tutor shows demonstration in front of a robot tutee, and the robot tutee observes the demonstration for learning. The robot tutee perceives external environment through its camera, recognizes a human and objects, and figures out a state at which the robot tutee is situated. Then, the robot tutee gazes at proper targets that are predefined by the state machine. The human tutor also adjusts the demonstration to make learning more effectively according to the robot tutee’s feedbacks. The effectiveness of the proposed method is demonstrated through the experiments with a robotic head with 17 degrees of freedom, developed in the RIT Lab., KAIST.

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Acknowledgments

This work was partly supported by Institute for Information communications Technology Promotion (IITP) grant funded by the Korea government (MSIP) (No. B0101-15-0551, Virtual Creatures with Digital DNA), the National Research Foundation of Korea (NRF) grant funded by the Korea government (MSIP) (No. NRF-2014R1A2A1A10051551), and the Technology Innovation Program, 10045252, Development of robot task intelligence technology, supported by the Ministry of Trade, Industry, and Energy (MOTIE, Korea).

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Authors and Affiliations

  1. School of Electrical Engineering, KAIST, 291 DaeHak-ro, Yuseong-gu, Daejeon, Republic of Korea

    Bum-Soo Yoo & Jong-Hwan Kim

Authors
  1. Bum-Soo Yoo
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  2. Jong-Hwan Kim
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Corresponding author

Correspondence to Bum-Soo Yoo .

Editor information

Editors and Affiliations

  1. Electrical Engineering, Korea Advanced Institute of Science & Technology, Daejeon, Korea (Republic of)

    Jong-Hwan Kim

  2. Department of Electrical and Computer Engineering, University of Waterloo, Waterloo, Ontario, Canada

    Fakhri Karray

  3. School of Information and Communication Technology, Griffith University, Gold Coast, Queensland, Australia

    Jun Jo

  4. Center for Intelligent Technologies, Technical University of Kosice, Kosice, Slovakia

    Peter Sincak

  5. Civil and Environmental Engineering, Korea Advanced Institute of Science & Technology, Daejeon, Korea (Republic of)

    Hyun Myung

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Yoo, BS., Kim, JH. (2017). Gaze Control of Humanoid Robot for Learning from Demonstration. In: Kim, JH., Karray, F., Jo, J., Sincak, P., Myung, H. (eds) Robot Intelligence Technology and Applications 4. Advances in Intelligent Systems and Computing, vol 447. Springer, Cham. https://doi.org/10.1007/978-3-319-31293-4_21

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  • DOI: https://doi.org/10.1007/978-3-319-31293-4_21

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-319-31291-0

  • Online ISBN: 978-3-319-31293-4

  • eBook Packages: EngineeringEngineering (R0)

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