Owing to the recent success of Large Language Models, Modern A.I has been much focused on linguistic interactions with humans but less focused on non-linguistic forms of communication between man and machine. In the present paper, we test how affective-linguistic communication, in combination with differential outcomes training, affects mutual learning in a human-robot context. Taking inspiration from child-caregiver dynamics, our human-robot interaction setup consists of a (simulated) robot attempting to learn how best to communicate internal, homeostatically-controlled needs; while a human "caregiver" attempts to learn the correct object to satisfy the robot's present communicated need. We studied the effects of i) human training type, and ii) robot reinforcement learning type, to assess mutual learning terminal accuracy and rate of learning (as measured by the average reward achieved by the robot). Our results find mutual learning between a human and a robot is significantly improved with Differential Outcomes Training (DOT) compared to Non-DOT (control) conditions. We find further improvements when the robot uses an exploration-exploitation policy selection, compared to purely exploitation policy selection. These findings have implications for utilizing socially assistive robots (SAR) in therapeutic contexts, e.g. for cognitive interventions, and educational applications.

本文研究了情感-语言交流在人机交互中的影响以及差异性结果训练对于人和机器之间的相互学习的影响，发现使用差异性结果训练以及探索-开发政策选择可以显著提高人和机器之间的相互学习，这对于社交辅助机器人在认知干预和教育应用等治疗场景中具有重要意义。

通过情感语言交互和差异结果训练进行人机相互学习