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：我们提出、分析并实验验证了一种新的机器人社交导航主动方法，该方法由机器人对如何避让及避让幅度形成的“观点”驱动，用于穿越其路径上移动的行人。机器人根据非线性动力学随时间形成观点，该动力学依赖于机器人对行人的观测及其对这些社交线索的关注程度。对于该动力学，可证明当机器人关注度超过临界值时，决策僵局将被打破，机器人迅速形成强烈观点，即使机器人对避让方向既无偏好也无证据，仍能成功避让每个移动行人。我们通过使机器人在行人接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，并验证了关于打破决策僵局的分析结论。我们还表明，单个设计参数即可调节人机避让过程中效率与可靠性之间的权衡。该方法兼具无需依赖人类行为预测模型的额外优势。