In social robotics, endowing humanoid robots with the ability to generate bodily expressions of affect can improve human-robot interaction and collaboration, since humans attribute, and perhaps subconsciously anticipate, such traces to perceive an agent as engaging, trustworthy, and socially present. Robotic emotional body language needs to be believable, nuanced and relevant to the context. We implemented a deep learning data-driven framework that learns from a few hand-designed robotic bodily expressions and can generate numerous new ones of similar believability and lifelikeness. The framework uses the Conditional Variational Autoencoder model and a sampling approach based on the geometric properties of the model's latent space to condition the generative process on targeted levels of valence and arousal. The evaluation study found that the anthropomorphism and animacy of the generated expressions are not perceived differently from the hand-designed ones, and the emotional conditioning was adequately differentiable between most levels except the pairs of neutral-positive valence and low-medium arousal. Furthermore, an exploratory analysis of the results reveals a possible impact of the conditioning on the perceived dominance of the robot, as well as on the participants' attention.
翻译:在社会机器人中,赋予人体机器人以产生身体影响表现的能力,可以改善人体-机器人的互动和合作,因为人类属性,或许还有潜意识地预测,这些痕迹可以将一个物剂视为具有参与性、可信赖性和社会存在。机器人情感身体语言需要可以想象、有细微和与上下文相关。我们实施了一个深层次的学习数据驱动框架,从几个手工设计的机器人身体表现中学习,并可以产生许多类似易感性和生命相似性的新数据。这个框架使用保守性变异自动coder模型和基于模型潜在空间的几何特性的抽样方法,使基因化过程以有目标的价值和振奋程度为条件。评估研究发现,人类形态学和生成的表达的灵敏性与手设计的表达方式并不不同,除了中性-正值和低中度振奋度的配方外,大多数级别之间的情感调节是完全不同的。此外,一个基于模型潜质空间的模型模型模型模型和取样方法的取样方法,使基因变异性过程以有目标的数值和振奋度为基础,从而揭示了对被观察到的机器人参与者的视向性影响。