Social robots are increasingly being designed for interactions that are intuitive and engaging with humans, requiring behaviors that must not only be well-timed and appropriate but also sophisticated and diverse. Current robotics often stands challenged by the need for robots that are not merely reactive but are engaging through behaviors characterized as truly "social"—encompassing two-way interactions and a deep social awareness. This demand necessitates a continuous evolution from basic programmed responses to dynamically generated behaviors that effectively mirror human social cues and contexts.
This Research Topic centers on refining the techniques and methodologies necessary for pushing the boundaries of autonomous multimodal behavior generation in social robots. The primary goal is to evolve robot behaviors from static, pre-programmed sets to dynamic, learning-based models that leverage advances in machine learning, affective computing, and real-time human interaction data. Through employing sophisticated algorithms and new computational approaches, the objective is to foster robots capable of interpreting complex human social signals and responding in a contextually appropriate and socially relevant manner.
To cover the expansive potential of this research, a delineated scope includes the immediate focus on autonomous generation systems and user-centered design principles. We invite contributions dealing with, but not limited to, the following themes:
- Design methods that incorporate user feedback for real-time behavior adaptation.
- Generative models for synthesizing context-specific behaviors using advanced algorithms like GANs.
- New datasets for training and validating robot behaviors in social contexts.
- Planning algorithms for decision-making in social interactions.
- Cognitive architectures that mimic human social processing capabilities.
- Multimodal frameworks for seamless human-robot interactions.
- Adaptation and personalization techniques for enhancing interactivity based on user-specific feedback.
Keywords:
social robot, behavior generation, multimodal behavior, deep learning, generative model, interactive behaviors
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.
Social robots are increasingly being designed for interactions that are intuitive and engaging with humans, requiring behaviors that must not only be well-timed and appropriate but also sophisticated and diverse. Current robotics often stands challenged by the need for robots that are not merely reactive but are engaging through behaviors characterized as truly "social"—encompassing two-way interactions and a deep social awareness. This demand necessitates a continuous evolution from basic programmed responses to dynamically generated behaviors that effectively mirror human social cues and contexts.
This Research Topic centers on refining the techniques and methodologies necessary for pushing the boundaries of autonomous multimodal behavior generation in social robots. The primary goal is to evolve robot behaviors from static, pre-programmed sets to dynamic, learning-based models that leverage advances in machine learning, affective computing, and real-time human interaction data. Through employing sophisticated algorithms and new computational approaches, the objective is to foster robots capable of interpreting complex human social signals and responding in a contextually appropriate and socially relevant manner.
To cover the expansive potential of this research, a delineated scope includes the immediate focus on autonomous generation systems and user-centered design principles. We invite contributions dealing with, but not limited to, the following themes:
- Design methods that incorporate user feedback for real-time behavior adaptation.
- Generative models for synthesizing context-specific behaviors using advanced algorithms like GANs.
- New datasets for training and validating robot behaviors in social contexts.
- Planning algorithms for decision-making in social interactions.
- Cognitive architectures that mimic human social processing capabilities.
- Multimodal frameworks for seamless human-robot interactions.
- Adaptation and personalization techniques for enhancing interactivity based on user-specific feedback.
Keywords:
social robot, behavior generation, multimodal behavior, deep learning, generative model, interactive behaviors
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.