Handing objects to humans is an essential capability for collaborative robots. Previous research works on human-robot handovers focus on facilitating the performance of the human partner and possibly minimising the physical effort needed to grasp the object. However, altruistic robot behaviours may result in protracted and awkward robot motions, contributing to unpleasant sensations by the human partner and affecting perceived safety and social acceptance. This paper investigates whether transferring the cognitive science principle that "humans act coefficiently as a group" (i.e. simultaneously maximising the benefits of all agents involved) to human-robot cooperative tasks promotes a more seamless and natural interaction. Human-robot coefficiency is first modelled by identifying implicit indicators of human comfort and discomfort as well as calculating the robot energy consumption in performing the desired trajectory. We then present a reinforcement learning approach that uses the human-robot coefficiency score as reward to adapt and learn online the combination of robot interaction parameters that maximises such coefficiency. Results proved that by acting coefficiently the robot could meet the individual preferences of most subjects involved in the experiments, improve the human perceived comfort, and foster trust in the robotic partner.

翻译：将物体传递给人类是协作机器人的一项关键能力。以往关于人机交接的研究主要侧重于提升人类伙伴的操作表现，并尽可能减少其抓取物体所需的体力消耗。然而，利他性的机器人行为可能导致动作冗长且笨拙，引发人类伙伴的不适感，并影响其感知安全性与社会接受度。本文探讨了将认知科学原理——“人类作为群体协同行动”（即同时最大化所有参与主体的利益）——迁移至人机协作任务中，是否能够促进更顺畅、更自然的交互。首先，通过识别人类舒适与不适的隐性指标，并计算机器人执行预期轨迹时的能耗，对人机协同效率进行建模。随后，我们提出一种强化学习方法，以人机协同效率得分作为奖励函数，在线学习和自适应调整最大化该协同效率的机器人交互参数组合。实验结果表明，通过协同行动，机器人能够满足实验中大多数受试者的个性化偏好，提升人类感知的舒适度，并增强对机器人伙伴的信任。