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Tsinghua Science and Technology 2019, 24(6): 654-662 https://doi.org/10.26599/TST.2018.9010096
Open Access | Issue | Published: 05 December 2019

Skill Learning for Human-Robot Interaction Using Wearable Device

Show Author's Information Bin Fang*( ),  Xiang Wei,  Fuchun Sun,  Haiming Huang,  Yuanlong Yu,  Huaping Liu
Department of Computer Science and Technology, Tsinghua University, Beijing 100084, China.
College of Information Engineering, Shenzhen University, Shenzhen 518060, China.
Fuzhou University, Fuzhou 350108, China.
Keywords:
interaction, skill learning, teleoperation, dynamical movement primitive
Cite this article:
Fang B, Wei X, Sun F, et al. Skill Learning for Human-Robot Interaction Using Wearable Device. Tsinghua Science and Technology, 2019, 24(6): 654-662. https://doi.org/10.26599/TST.2018.9010096
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Abstract Full text About this article

With the accelerated aging of the global population and escalating labor costs, more service robots are needed to help people perform complex tasks. As such, human-robot interaction is a particularly important research topic. To effectively transfer human behavior skills to a robot, in this study, we conveyed skill-learning functions via our proposed wearable device. The robotic teleoperation system utilizes interactive demonstration via the wearable device by directly controlling the speed of the motors. We present a rotation-invariant dynamical-movement-primitive method for learning interaction skills. We also conducted robotic teleoperation demonstrations and designed imitation learning experiments. The experimental human-robot interaction results confirm the effectiveness of the proposed method.


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About this article

Skill Learning for Human-Robot Interaction Using Wearable Device

Show Author's information Bin Fang*( ), Xiang Wei, Fuchun Sun, Haiming Huang, Yuanlong Yu, Huaping Liu
Department of Computer Science and Technology, Tsinghua University, Beijing 100084, China.
College of Information Engineering, Shenzhen University, Shenzhen 518060, China.
Fuzhou University, Fuzhou 350108, China.

Abstract

With the accelerated aging of the global population and escalating labor costs, more service robots are needed to help people perform complex tasks. As such, human-robot interaction is a particularly important research topic. To effectively transfer human behavior skills to a robot, in this study, we conveyed skill-learning functions via our proposed wearable device. The robotic teleoperation system utilizes interactive demonstration via the wearable device by directly controlling the speed of the motors. We present a rotation-invariant dynamical-movement-primitive method for learning interaction skills. We also conducted robotic teleoperation demonstrations and designed imitation learning experiments. The experimental human-robot interaction results confirm the effectiveness of the proposed method.

Keywords: interaction, skill learning, teleoperation, dynamical movement primitive

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Publication history
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Publication history

Received: 09 February 2018
Revised: 13 April 2018
Accepted: 27 April 2018
Published: 05 December 2019
Issue date: December 2019

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© The author(s) 2019

Acknowledgements

This work was supported by the National Natural Science Foundation of China (Nos. 61503212, 61473089, U1613212, and 61327809), the Beijing Science and Technology Program (No. Z171100000817007), the German Research Foundation (DFG) in project Cross Modal Learning (No. NSFC 61621136008/DFG TRR-169), and the Suzhou Special Program (No. 2016SZ0219).

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