An outstanding challenge with safety methods for human-robot interaction is reducing their conservatism while maintaining robustness to variations in human behavior. In this work, we propose that robots use confidence-aware game-theoretic models of human behavior when assessing the safety of a human-robot interaction. By treating the influence between the human and robot as well as the human's rationality as unobserved latent states, we succinctly infer the degree to which a human is following the game-theoretic interaction model. We leverage this model to restrict the set of feasible human controls during safety verification, enabling the robot to confidently modulate the conservatism of its safety monitor online. Evaluations in simulated human-robot scenarios and ablation studies demonstrate that imbuing safety monitors with confidence-aware game-theoretic models enables both safe and efficient human-robot interaction. Moreover, evaluations with real traffic data show that our safety monitor is less conservative than traditional safety methods in real human driving scenarios.

翻译：人类机器人互动安全方法的一个突出挑战是降低人类机器人互动安全方法的保守性,同时保持对人类行为差异的稳健性。 在这项工作中,我们建议机器人在评估人类机器人互动安全性时使用具有信心的游戏理论模型。 通过将人类与机器人之间的影响以及人类理性作为未观测到的潜伏状态来处理,我们简洁地推断人遵循游戏理论互动模式的程度。我们利用这个模型限制安全核查期间可行的人类控制,使机器人能够自信地在线调节其安全监测器的保守性。模拟人类机器人假设情景和反动研究的评估表明,将安全监测器与具有信心的游戏理论模型联系起来,既能安全又能高效地进行人类机器人互动。此外,对真实交通数据的评估表明,在真实的人类驱动情景中,我们的安全监测器比传统的安全方法保守。