Machines that are sensitive to environmental fluctuations, such as autonomous and pet robots, are currently in demand, rendering the ability to control huge and complex systems crucial. However, controlling such a system in its entirety using only one control device is difficult; for this purpose, a system must be both diverse and flexible. Herein, we derive and analyze the feature values of robot sensor and actuator data, thereby investigating the role that each feature value plays in robot locomotion. We conduct experiments using a developed quadruped robot from which we acquire multi-point motion information as the movement data; we extract the features of these movement data using an autoencoder. Next, we decompose the movement data into three features and extract various gait patterns. Despite learning only the “walking” movement, the movement patterns of trotting and bounding are also extracted herein, which suggests that movement data obtained via hardware contain various gait patterns. Although the present robot cannot locomote with these movements, this research suggests the possibility of generating unlearned movements.

中文翻译：

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通过学习专门的运动数据来概括四足机器人运动中的运动

当前需要对环境波动敏感的机器，例如自动和宠物机器人，因此控制大型复杂系统的能力至关重要。但是，仅使用一个控制装置来整体控制这样的系统是困难的。为此，系统必须既多样化又灵活。本文中，我们推导并分析了机器人传感器和执行器数据的特征值，从而研究了每个特征值在机器人运动中的作用。我们使用发达的四足机器人进行实验，从中获取多点运动信息作为运动数据。我们使用自动编码器提取这些运动数据的特征。接下来，我们将运动数据分解为三个特征，并提取各种步态模式。尽管仅学习“步行”运动，在此还提取了小跑和边界的运动模式，这表明通过硬件获得的运动数据包含各种步态模式。尽管目前的机器人无法适应这些运动，但这项研究表明可能产生未经学习的运动。