Generation of Human-Like Movement from Symbolized Information

Frontiers in Neurorobotics

Fady S. Alnajjar¹  Yasuhisa Hasegawa²  Mitsuhiro Hayashibe³  Maxime Tournier⁴  Shotaro Okajima⁴  Shingo Shimoda⁴ 

[1] College of IT, United Arab Emirates University, Al-Ain, United Arab Emirates;Department of Mechanical Science and Engineering, Graduate School of Engineering, Nagoya University, Nagoya, Japan;Department of Robotics, Tohoku University, Sendai, Japan;Intelligent Behavior Control Unit (BTCC), Brain Science Institute (BSI), RIKEN, Nagoya, Japan;

关键词: mechanical resonance mode;    tacit learning;    control structure;    symbolized information;    human-like movement;   

DOI  :  10.3389/fnbot.2018.00043

来源: DOAJ

【 摘 要 】

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.

【 授权许可】

Unknown