Recognizing and Generating Novel Emotional Behaviors on Two Robotic Platforms

Recent advancements in language modeling have enabled robots to more easily generate complex behaviors. However, ensuring that the generated behaviors align with the intended emotional states of the robot is necessary in many domains where robots are used. In this paper, we present an adversarial-like training regime in which a generative model of emotional behavior is enhanced through feedback from both an emotion discriminator and a novelty loss, to ensure that the generated behaviors are non-redundant. Our generative model, fine-tuned on a dataset of robot behaviors labeled with emotions, generates behavior sequences perceived as reflecting the emotional qualities of the input emotion labels. Through our training regime, the generative model is refined by minimizing the discrepancies in both emotion classification and behavioral novelty. We evaluated our approach through multiple experiments and human evaluations, where participants were asked to appraise the emotions conveyed by robot behaviors and rate the novelty of the behaviors. Experimental results demonstrate that our two models, one for classifying and one for generating emotional behaviors, are effective, with the generative model producing emotionally rich behaviors that differ from previously generated outputs.