About this Research Topic
Advances in robotics technology and artificial intelligence have paved the way for a new paradigm of musical and entertainment robots, creating a multidisciplinary domain that involves the collaboration of experts from diverse scientific fields such as robotics, musicology, psychology, entertainment, ethnomusicology, neuroscience, computer science, and brain research.
These robots are designed to go beyond traditional service-oriented functions and focus on providing users with amusement, enjoyment, and entertainment in various interactive activities such as playing musical instruments, singing, dancing, and even composing music. Advanced technologies such as speech recognition, natural language processing (NLP), computer vision, and machine learning have been incorporated to support robot interaction with users and to adapt their performances based on user preferences and reactions. For robots, achieving human-like expressiveness in musical applications has been a grand challenge. Music is a highly nuanced and expressive art form that involves elements such as dynamics, phrasing, emotion and interpretation, which are intricately linked to human perception and experience.
This Research Topic focuses on the development of AI-powered musical and entertainment robots. The collection aims to highlight the challenges of achieving human-like expressiveness and skill learning in robots for artistic musical applications. We encourage contributions exploring AI techniques to develop robots capable of understanding musical cues, generating expressive performances, engaging with audiences, and adapting their behavior dynamically.
Topics of interest include, but are not limited to:
• Robotic entertainment
• Computer and robot vision for entertainment robotics
• AI-based methods for robotic music
• Cognitive robotics for entertainment
• Educational robotics
• Human factors and human-in-the-Loop
• Self-organized system
• Sensory motor coordination
• Simulation and visualization for entertainment robotics
• Automatic music generation (AMG) in robotics
• Collaborative music improvisation
• AR/VR Integration for Immersive Performances
• Acceptability and trust in robotics
These robots are designed to go beyond traditional service-oriented functions and focus on providing users with amusement, enjoyment, and entertainment in various interactive activities such as playing musical instruments, singing, dancing, and even composing music. Advanced technologies such as speech recognition, natural language processing (NLP), computer vision, and machine learning have been incorporated to support robot interaction with users and to adapt their performances based on user preferences and reactions. For robots, achieving human-like expressiveness in musical applications has been a grand challenge. Music is a highly nuanced and expressive art form that involves elements such as dynamics, phrasing, emotion and interpretation, which are intricately linked to human perception and experience.
This Research Topic focuses on the development of AI-powered musical and entertainment robots. The collection aims to highlight the challenges of achieving human-like expressiveness and skill learning in robots for artistic musical applications. We encourage contributions exploring AI techniques to develop robots capable of understanding musical cues, generating expressive performances, engaging with audiences, and adapting their behavior dynamically.
Topics of interest include, but are not limited to:
• Robotic entertainment
• Computer and robot vision for entertainment robotics
• AI-based methods for robotic music
• Cognitive robotics for entertainment
• Educational robotics
• Human factors and human-in-the-Loop
• Self-organized system
• Sensory motor coordination
• Simulation and visualization for entertainment robotics
• Automatic music generation (AMG) in robotics
• Collaborative music improvisation
• AR/VR Integration for Immersive Performances
• Acceptability and trust in robotics
Keywords: human-robot interaction, musical and entertainment robots, machine learning, expressive performance, audience engagement
Important Note: All contributions to this Research Topic must be within the scope of the section and journal to which they are submitted, as defined in their mission statements. Frontiers reserves the right to guide an out-of-scope manuscript to a more suitable section or journal at any stage of peer review.
