Human–Robot Cooperative Piano Playing With Learning-Based Real-Time Music Accompaniment

IF 9.4 1区 计算机科学 Q1 ROBOTICS
Huijiang Wang;Xiaoping Zhang;Fumiya Iida
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Abstract

Recent advances in machine learning have paved the way for the development of musical and entertainment robots. However, human–robot cooperative instrument playing remains a challenge, particularly due to the intricate motor coordination and temporal synchronization. In this article, we propose a theoretical framework for human–robot cooperative piano playing based on nonverbal cues. First, we present a music improvisation model that employs a recurrent neural network (RNN) to predict appropriate chord progressions based on the human's melodic input. Second, we propose a behavior-adaptive controller to facilitate seamless temporal synchronization, allowing the cobot to generate harmonious acoustics. The collaboration takes into account the bidirectional information flow between the human and robot. We have developed an entropy-based system to assess the quality of cooperation by analyzing the impact of different communication modalities during human–robot collaboration. Experiments demonstrate that our RNN-based improvisation can achieve a 93% accuracy rate. Meanwhile, with the MPC adaptive controller, the robot could respond to the human teammate in homophony performances with real-time accompaniment. Our designed framework has been validated to be effective in allowing humans and robots to work collaboratively in the artistic piano-playing task.
利用基于学习的实时音乐伴奏进行人机合作钢琴演奏
机器学习领域的最新进展为音乐和娱乐机器人的开发铺平了道路。然而,人机合作乐器演奏仍然是一项挑战,特别是由于复杂的运动协调和时间同步。在本文中,我们提出了一个基于非语言线索的人机合作钢琴演奏理论框架。首先,我们提出了一个音乐即兴演奏模型,该模型采用递归神经网络(RNN),根据人类的旋律输入预测适当的和弦行进。其次,我们提出了一种行为自适应控制器,以促进无缝的时间同步,让 cobot 产生和谐的音响效果。这种协作考虑到了人类与机器人之间的双向信息流。我们开发了一种基于熵的系统,通过分析人类与机器人协作过程中不同通信方式的影响来评估合作质量。实验证明,我们基于 RNN 的即兴创作可以达到 93% 的准确率。同时,通过 MPC 自适应控制器,机器人可以在同音表演中实时响应人类队友的伴奏。经过验证,我们设计的框架能有效地让人类和机器人在艺术钢琴演奏任务中协同工作。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
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来源期刊
IEEE Transactions on Robotics
IEEE Transactions on Robotics 工程技术-机器人学
CiteScore
14.90
自引率
5.10%
发文量
259
审稿时长
6.0 months
期刊介绍: The IEEE Transactions on Robotics (T-RO) is dedicated to publishing fundamental papers covering all facets of robotics, drawing on interdisciplinary approaches from computer science, control systems, electrical engineering, mathematics, mechanical engineering, and beyond. From industrial applications to service and personal assistants, surgical operations to space, underwater, and remote exploration, robots and intelligent machines play pivotal roles across various domains, including entertainment, safety, search and rescue, military applications, agriculture, and intelligent vehicles. Special emphasis is placed on intelligent machines and systems designed for unstructured environments, where a significant portion of the environment remains unknown and beyond direct sensing or control.
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