Okajima Shotaro, Tournier Maxime, Alnajjar Fady S, Hayashibe Mitsuhiro, Hasegawa Yasuhisa, Shimoda Shingo
Department of Mechanical Science and Engineering, Graduate School of Engineering, Nagoya University, Nagoya, Japan.
Intelligent Behavior Control Unit (BTCC), Brain Science Institute (BSI), RIKEN, Nagoya, Japan.
Front Neurorobot. 2018 Jul 17;12:43. doi: 10.3389/fnbot.2018.00043. eCollection 2018.
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.
当前机器人系统缺少一项重要功能，即一种从符号化信息创建行为的类人方法。该功能可用于评估机器人行为的类人程度，因为它能区分人类运动与使用现有技术制造的人造机器的运动。本研究的目的是阐明生成自动运动指令以实现符号化行为的机制。我们设计了一种控制器，它具有一种名为隐性学习的学习方法，该方法考虑系统与环境的相互作用，以及一种名为机械共振模式的转换方法，该方法将控制信号转换到机械共振模式空间（MRM空间）。我们进行了模拟和实验，包括使用两自由度倒立摆进行抗干扰的站立平衡控制以及使用人形机器人进行双足行走控制。在站立平衡控制的模拟和实验中，通过隐性学习在MRM空间中调整几个信号，摆锤在受到干扰后可以恢复直立。在双足行走控制的模拟和实验中，机器人通过手动在MRM空间中调整几个信号实现了多种行走方式。结果表明，将信号转换到合适的控制空间是降低来自环境的信号复杂性并实现多样行为的关键过程。
