We propose, analyze, and experimentally verify a new proactive approach for robot social navigation driven by the robot's "opinion" for which way and by how much to pass human movers crossing its path. The robot forms an opinion over time according to nonlinear dynamics that depend on the robot's observations of human movers and its level of attention to these social cues. For these dynamics, it is guaranteed that when the robot's attention is greater than a critical value, deadlock in decision making is broken, and the robot rapidly forms a strong opinion, passing each human mover even if the robot has no bias nor evidence for which way to pass. We enable proactive rapid and reliable social navigation by having the robot grow its attention across the critical value when a human mover approaches. With human-robot experiments we demonstrate the flexibility of our approach and validate our analytical results on deadlock-breaking. We also show that a single design parameter can tune the trade-off between efficiency and reliability in human-robot passing. The new approach has the additional advantage that it does not rely on a predictive model of human behavior.

翻译：本文提出、分析并实验验证了一种新型的机器人社交导航主动方法，该方法基于机器人的"意见"来决定以何种方式和幅度穿越其路径上的人类运动物体。机器人根据非线性动力学随时间形成意见，该动力学取决于机器人对人类运动物体的观测以及对这些社交线索的关注程度。对于这些动力学，理论上保证了当机器人关注度超过临界值时，决策过程中的死锁被打破，机器人能迅速形成强烈意见，即使机器人对穿越方向既无偏见也无证据，也能成功绕开每个人类运动物体。我们通过让机器人在人类运动物体靠近时将其关注度提升至临界值以上，实现了主动、快速且可靠的社交导航。通过人机实验，我们展示了该方法的灵活性，并验证了关于死锁破除的理论分析结果。我们还表明，单一设计参数即可调节人机避让中效率与可靠性之间的权衡。该方法另一优势在于无需依赖人类行为的预测模型。