Artificial curiosity, based on developmental psychology concepts wherein an agent attempts to maximize its learning progress, has gained much attention in recent years. Similarly, social robots are slowly integrating into our daily lives, in schools, factories, and in our homes. In this contribution, we integrate recent advances in artificial curiosity and social robots into a single expressive cognitive architecture. It is composed of artificial curiosity and social expressivity modules and their unique link, i.e., the robot verbally and non-verbally communicates its internally estimated learning progress, or learnability, to its human companion. We implemented this architecture in an interaction where a fully autonomous robot took turns with a child trying to select and solve tangram puzzles on a tablet. During the curious robot’s turn, it selected its estimated most learnable tangram to play, communicated its selection to the child, and then attempted at solving it. We validated the implemented architecture and showed that the robot learned, estimated its learnability, and improved when its selection was based on its learnability estimation. Moreover, we ran a comparison study between curious and non-curious robots, and showed that the robot’s curiosity-based behavior influenced the child’s selections. Based on the artificial curiosity module of the robot, we have formulated an equation that estimates each child’s moment-by-moment curiosity based on their selections. This analysis revealed an overall significant decrease in estimated curiosity during the interaction. However, this drop in estimated curiosity was significantly larger with the non-curious robot, compared to the curious one. These results suggest that the new architecture is a promising new approach to integrate state-of-the-art curiosity-based algorithms to the growing field of social robots.

中文翻译：

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一个好奇的社交机器人的表达认知架构

人工好奇心基于发展心理学概念，其中代理试图最大化其学习进度，近年来受到了广泛关注。同样，社交机器人正在慢慢融入我们的日常生活、学校、工厂和我们的家中。在这篇文章中，我们将人工好奇心和社交机器人的最新进展整合到一个单一的表达认知架构中。它由人工好奇心和社会表达模块及其独特的链接组成，即机器人以口头和非口头的方式将其内部估计的学习进度或可学习性传达给它的人类同伴。我们在交互中实现了这种架构，其中一个完全自主的机器人轮流与一个孩子尝试在平板电脑上选择和解决七巧板拼图。轮到好奇的机器人时，它选择了它估计的最易学的七巧板来玩，将它的选择传达给孩子，然后尝试解决它。我们验证了实现的架构，并表明机器人学习、估计了其可学习性，并在其选择基于其可学习性估计时进行了改进。此外，我们对好奇和不好奇的机器人进行了比较研究，结果表明机器人基于好奇心的行为影响了孩子的选择。基于机器人的人工好奇心模块，我们制定了一个方程，根据每个孩子的选择来估计每个孩子的时刻好奇心。该分析显示，在交互过程中，估计的好奇心总体上显着下降。然而，对于不好奇的机器人，这种估计好奇心的下降要大得多，与好奇的相比。这些结果表明，新架构是一种很有前途的新方法，可以将最先进的基于好奇心的算法集成到不断发展的社交机器人领域。