Dynamic motion generation by flexible-joint robot based on deep learning using images

Yuheng Wu, Kuniyuki Takahashi, Hiroki Yamada, Kitae Kim, Shingo Murata, Shigeki Sugano, Tetsuya Ogata

Research output: Chapter in Book/Report/Conference proceedingConference contribution

1 Citation (Scopus)

Abstract

Robots with flexible joints have recently been attracting attention from researchers because such robots can passively adapt to environmental changes and realize dynamic motion that uses inertia. In previous research, body-model acquisition using deep learning was proposed and dynamic motion learning was achieved. However, using the end-effector position as a visual feedback signal to train a robot limits what the robot can know to only the relation between the task and itself, instead of the relation between the environment and itself. In this research, we propose to use images as a feedback signal so that the robot can have a sense of the overall situation within the task environment. This motion learning is performed via deep learning using raw image data. In an experiment, we let a robot perform task motions once to acquire motor and image data. Then, we used a convolutional auto-encoder to extract image features from raw image data. The extracted image features were used in combination with motor data to train a recurrent neural network. As a result, motion learning through deep learning from image data allowed the robot to acquire environmental information and conduct tasks that require consideration of environmental changes, making use of its advantage of passive adaptation.

Original languageEnglish
Title of host publication2018 Joint IEEE 8th International Conference on Development and Learning and Epigenetic Robotics, ICDL-EpiRob 2018
PublisherInstitute of Electrical and Electronics Engineers Inc.
Pages169-174
Number of pages6
ISBN (Electronic)9781538661109
DOIs
Publication statusPublished - 2018 Sep
EventJoint 8th IEEE International Conference on Development and Learning and Epigenetic Robotics, ICDL-EpiRob 2018 - Tokyo, Japan
Duration: 2018 Sep 162018 Sep 20

Publication series

Name2018 Joint IEEE 8th International Conference on Development and Learning and Epigenetic Robotics, ICDL-EpiRob 2018

Conference

ConferenceJoint 8th IEEE International Conference on Development and Learning and Epigenetic Robotics, ICDL-EpiRob 2018
CountryJapan
CityTokyo
Period18/9/1618/9/20

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ASJC Scopus subject areas

  • Computer Vision and Pattern Recognition
  • Control and Optimization
  • Behavioral Neuroscience
  • Developmental Neuroscience
  • Artificial Intelligence

Cite this

Wu, Y., Takahashi, K., Yamada, H., Kim, K., Murata, S., Sugano, S., & Ogata, T. (2018). Dynamic motion generation by flexible-joint robot based on deep learning using images. In 2018 Joint IEEE 8th International Conference on Development and Learning and Epigenetic Robotics, ICDL-EpiRob 2018 (pp. 169-174). [8761020] (2018 Joint IEEE 8th International Conference on Development and Learning and Epigenetic Robotics, ICDL-EpiRob 2018). Institute of Electrical and Electronics Engineers Inc.. https://doi.org/10.1109/DEVLRN.2018.8761020