EVOLVE: Emotion and Visual Output Learning via LLM Evaluation

Additionally, this kind of subdivided action While the ability to effectively communicate and retain schema can be used to evaluate many attributes towards user attention for longer periods of time is important in many promoting empathetic responses, including tone of voice, HRI settings, eliciting an impression of empathy through nonverbal cues, and facial expressions [6]. However, atomic nonverbal behavior can be critical to acceptance of and trust actions with limited sentiments might not be sufficient to in social robots [1]. Through a comprehensive survey over accommodate complex emotion in the user. This work investigates several LLM-based actions, [2] discovered that social robots the possibility of a more open-ended response elicited higher expectations for more nuanced nonverbal cues selection by leveraging an LLM's internal domain knowledge including a breadth of behavior types. Conveying affects that of emojis and other affective imagery capable of representing are aligned with the user's emotional state can be critical emotional states. We also employ recent advances in visionlanguage in building trust around experienced empathy and personalization models with an image or camera input, as suggested from a social robot [3]. Multi-modal feedback have in [2] and [4]. Additionally, we evaluate both motion and profound impacts on successful empathetic interaction, as color [7] pattern elicitation through atomic action selection notions inferred from robot actions can be understood much [5], [6]. We selected these decision categories based on a easier with systematic actions taken in alignment with an theoretical robot design that could contain an LED strip emotional response [2], [4].