Generation of human-like movement from symbolized information

Shotaro Okajima, Maxime Tournier, Fady S. Alnajjar, Mitsuhiro Hayashibe, Yasuhisa Hasegawa, Shingo Shimoda

Research output: Contribution to journalArticlepeer-review

8 Citations (Scopus)


An important function missing from current robotic systems is a human-like method for creating behavior from symbolized information. This function could be used to assess the extent to which robotic behavior is human-like because it distinguishes human motion from that of human-made machines created using currently available techniques. The purpose of this research is to clarify the mechanisms that generate automatic motor commands to achieve symbolized behavior. We design a controller with a learning method called tacit learning, which considers system-environment interactions, and a transfer method called mechanical resonance mode, which transfers the control signals into a mechanical resonance mode space (MRM-space). We conduct simulations and experiments that involve standing balance control against disturbances with a two-degree-of-freedom inverted pendulum and bipedal walking control with humanoid robots. In the simulations and experiments on standing balance control, the pendulum can become upright after a disturbance by adjusting a few signals in MRM-space with tacit learning. In the simulations and experiments on bipedal walking control, the robots realize a wide variety of walking by manually adjusting a few signals in MRM-space. The results show that transferring the signals to an appropriate control space is the key process for reducing the complexity of the signals from the environment and achieving diverse behavior.

Original languageEnglish
Article number43
JournalFrontiers in Neurorobotics
Publication statusPublished - 2018


  • Control structure
  • Human-like movement
  • Mechanical resonance mode
  • Symbolized information
  • Tacit learning

ASJC Scopus subject areas

  • Biomedical Engineering
  • Artificial Intelligence


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