Communication robots often need to initiate conversations with people in public spaces. At the same time, such robots must not disturb pedestrians. To handle these two requirements, an agent needs to estimate the communication desires of others based on their behavior and then adjust its own communication activities accordingly. In this study, we construct a computational spatial interaction model that considers others. Consideration is expressed as a quantitative parameter: the amount of adjustment of one's internal state to the estimated internal state of the other. To validate the model, we experimented with a human and a virtual robot interacting in a VR environment. The results show that when the participant moves to the target, a virtual robot with a low consideration value inhibits the participant's movement, while a robot with a higher consideration value did not inhibit the participant's movement. When the participant approached the robot, the robot also exhibited approaching behavior, regardless of the consideration value, thus decreasing the participant's movement. These results appear to verify the proposed model's ability to clarify interactions with consideration for others.

翻译：在公共空间中，通信机器人常需主动发起与人的对话。与此同时，此类机器人不应干扰行人通行。为同时满足这两项要求，智能体需根据他人行为估算其沟通意愿，并据此调整自身的交互行为。本研究构建了一种考虑他人因素的计算化空间交互模型。其中“考虑”被量化为一个参数：个体根据对他人内在状态的估计值来调整自身内在状态的程度。为验证模型有效性，我们在虚拟现实环境中进行了人与虚拟机器人的交互实验。结果表明：当参与者向目标移动时，具有低考虑值的虚拟机器人会抑制参与者的移动，而考虑值较高的机器人则不会产生抑制效果。当参与者靠近机器人时，无论考虑值高低，机器人均会表现出趋近行为，从而减少参与者的移动距离。这些结果初步验证了所提模型在阐明“考虑他人”的交互机制方面的有效性。