Dance with a Robot: Encoder-Decoder Neural Network for Music-Dance Learning
Dance with a Robot: Encoder-Decoder Neural Network for Music-Dance Learning
复制标题
与机器人共舞：用于音乐舞蹈学习的编码器-解码器神经网络
DOI：
10.1145/3371382.3378372
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发表时间：
2020
期刊：
影响因子：
--
通讯作者：
C. Park
中科院分区：
文献类型：
--
作者：
Baijun Xie;C. Park
这份最新的报告提出了一种通过序列到序列（Seq2Seq）架构学习音乐和舞蹈之间的顺序和时间映射的方法。在这项研究中，Seq2Seq模型包括两个部分：用于处理音乐输入的编码器和用于生成输出运动矢量的解码器。该模型能够接受来自用户的音乐特征和动作输入，用于人机交互学习会话，该会话输出教导校正动作以跟随专家舞者的动作的动作模式。三种不同类型的Seq2Seq模型的结果进行了比较，并应用于仿真平台。该模型将应用于自闭症谱系障碍（ASD）儿童的社交互动场景。
This late-breaking report presents a method for learning sequential and temporal mapping between music and dance via the Sequence-to-Sequence (Seq2Seq) architecture. In this study, the Seq2Seq model comprises two parts: the encoder for processing the music inputs and the decoder for generating the output motion vectors. This model has the ability to accept music features and motion inputs from the user for human-robot interactive learning sessions, which outputs the motion patterns that teach the corrective movements to follow the moves from the expert dancer. Three different types of Seq2Seq models are compared in the results and applied to a simulation platform. This model will be applied in social interaction scenarios with children with autism spectrum disorder (ASD).