Although robots are becoming more advanced with human-like anthropomorphic features and decision-making abilities to improve collaboration, the active integration of humans into this process remains under-explored. This article presents the first experimental study exploring decision-making interactions between humans and robots with visual cues from robotic eyes, which can dynamically influence human opinion formation. The cues generated by robotic eyes gradually guide human decisions towards alignment with the robot's choices. Both human and robot decision-making processes are modeled as non-linear opinion dynamics with evolving biases. To examine these opinion dynamics under varying biases, we conduct numerical parametric and equilibrium continuation analyses using tuned parameters designed explicitly for the presented human-robot interaction experiment. Furthermore, to facilitate the transition from disagreement to agreement, we introduced a human opinion observation algorithm integrated with the formation of the robot's opinion, where the robot's behavior is controlled based on its formed opinion. The algorithms developed aim to enhance human involvement in consensus building, fostering effective collaboration between humans and robots. Experiments with 51 participants (N = 51) show that human-robot teamwork can be improved by guiding human decisions using robotic cues. Finally, we provide detailed insights on the effects of trust, cognitive load, and participant demographics on decision-making based on user feedback and post-experiment interviews.

翻译：尽管机器人正通过类人拟人化特征和决策能力变得更加先进以改善协作，但将人类主动整合到这一过程中的研究仍显不足。本文首次通过实验研究探索了人类与具备机器人视觉线索的机器人之间的决策交互，这些线索能够动态影响人类意见形成。机器人眼睛生成的线索逐步引导人类决策与机器人的选择趋于一致。人类和机器人的决策过程均被建模为具有演化偏置的非线性意见动力学。为检验不同偏置下的意见动力学，我们使用针对所提出的人机交互实验明确设计的调谐参数，进行了数值参数分析和平衡点延拓分析。此外，为促进从分歧到共识的转变，我们引入了与机器人意见形成相集成的人类意见观测算法，其中机器人的行为基于其形成的意见进行控制。所开发的算法旨在增强人类在共识构建中的参与度，促进人机之间的有效协作。对51名参与者（N = 51）的实验表明，通过机器人线索引导人类决策能够改善人机团队协作。最后，基于用户反馈和实验后访谈，我们详细分析了信任、认知负荷及参与者人口统计学特征对决策的影响。