Generation of Human-Like Movement from Symbolized Information

• Main Authors: Shotaro Okajima, Maxime Tournier, Fady S. Alnajjar, Mitsuhiro Hayashibe, Yasuhisa Hasegawa, Shingo Shimoda
• Format: Article
• Language: English
• Published: Frontiers Media S.A. 2018-07-01
• Series: Frontiers in Neurorobotics
• Subjects: mechanical resonance mode; tacit learning; control structure; symbolized information; human-like movement
• Online Access: https://www.frontiersin.org/article/10.3389/fnbot.2018.00043/full

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.