Numerous challenges underlying human-robot interaction exist. One such challenge is enabling robots to display human-like expressive behaviors. Traditional rule-based methods need more scalability in new social contexts, while the need for extensive, specific datasets limits data-driven approaches. This limitation becomes pronounced as the variety of social interactions a robot might encounter increases, creating a demand for more adaptable, context-sensitive solutions in robotic behavior programming. Research in generating socially acceptable robot and virtual human behaviors encompasses rule-based, template-based, and data-driven methods. Rule-based approaches rely on formalized rules but need expressivity and multimodal capabilities.…

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