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A Music-Driven Dance System of Humanoid Robots

机译:人形机器人的音乐驱动的舞蹈系统

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Robot dance is an important topic in robotics. Conventional robot dance systems mainly rely on beats or rhythms of music; however, these conventional systems suffer from limited dance styles and less action novelty. In this paper, we instead develop a humanoid robot dance system driven by musical structures and emotions. In the proposed system, a musical phrase and a dance phrase are considered as the basic structural units of music and dance, respectively. A musical phrasing algorithm based on music theories is created to divide a piece of music into a sequence of phrases. When the emotion of each phrase has been recognized, an emotion sequence can be established. Meanwhile, a hidden Markov model (HMM) matches a dance phrase sequence to the emotion sequence. In particular, several concepts of the "chance method" created by choreographer Merce Cunningham are adopted to guide our robot dance system; thus, a dance phrase is choreographed by randomly selecting and combining a number of actions from a predesigned action library. Based on the approach, one music can generate diverse robotic dance motions, showing the novelty and diversity of robot dance. The experiments on our humanoid robot "Alphal Pro" show that our robot can do a good job dancing to music according to musical structures and emotions and can be well accepted by various people.
机译:机器人舞蹈是机器人技术中的一个重要主题。传统的机器人舞蹈系统主要依靠音乐的节奏或节奏。但是,这些常规系统的舞蹈风格有限,动作新颖性较差。在本文中,我们改为开发由音乐结构和情感驱动的人形机器人跳舞系统。在提出的系统中,音乐短语和舞蹈短语分别被视为音乐和舞蹈的基本结构单元。创建了一种基于音乐理论的乐句算法,将一段音乐划分为一系列乐句。当已经识别出每个短语的情绪时,可以建立情绪序列。同时,隐马尔可夫模型(HMM)将舞蹈短语序列与情感序列进行匹配。尤其是,由编舞者Merce Cunningham提出的“机会方法”的几个概念被用来指导我们的机器人舞蹈系统。因此,通过从预先设计的动作库中随机选择并组合多个动作来对舞蹈短语进行编排。在这种方法的基础上,一种音乐可以产生各种机器人舞蹈动作,显示出机器人舞蹈的新颖性和多样性。在我们的类人机器人“ Alphaal Pro”上进行的实验表明,我们的机器人可以根据音乐的结构和情感很好地随着音乐跳舞,并且可以为各种人所接受。

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