基于环境信号积累的隐性学习稳定性分析

S. Shimoda, Y. Yoshihara, K. Fujimoto, Takashi Yamamoto, Iwao Maeda, H. Kimura
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引用次数: 11

摘要

隐性学习是一种基于生物控制原理来创造适应环境的适当行为的新型学习方案。在行为适应过程中,信号积累是隐性学习的关键因素。为了明确信号积累在学习过程中的作用,我们根据控制速度将其分为两个过程进行分析。快速过程用于行为控制,缓慢过程用于行为适应环境。我们利用积分器开发了用于隐性学习的连续时间控制器,并证明了信号积累可以通过机器人身体与环境的相互作用来估计机器人模型的一部分。这种隐性学习的能力对于控制一个工厂是有用的,其中建模错误和模型更改是稳定控制的关键问题。作为控制这类植物的突出例子,我们通过实验验证了隐性学习可以创造出在与地面失去接触的瞬间通过支撑腿推动地面的两足行走步态。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Stability analysis of tacit learning based on environmental signal accumulation
Tacit learning is the novel learning scheme based on the principle of biological control to create the appropriate behaviors adapted to the environment. Signal accumulation is the key factor for tacit learning in the process of behavior adaptation. To clarify the role of the signal accumulation in the learning process, we analyzed it dividing into the two processes depending on the control speed. The fast process is used for the behavior control and the slow process is used for the behavior adaptation to the environment. We developed the continuous-time controller for tacit learning with the integrators and showed that the signal accumulation can estimate a part of the robot model through the interactions between the robot body and the environment. This capability of tacit learning is useful to control a plant where the modeling errors and model changes are the critical problems for the stable controls. As the prominent example of the control of such plant, we experimentally verified that tacit learning can create the bipedal walking gait that pushes the ground by the support leg at the moment of losing contact with the ground.
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