For robots to interact socially, they must interpret human intentions and anticipate their potential outcomes accurately. This is particularly important for social robots designed for human care, which may face potentially dangerous situations for people, such as unseen obstacles in their way, that should be avoided. This paper explores the Artificial Theory of Mind (ATM) approach to inferring and interpreting human intentions. We propose an algorithm that detects risky situations for humans, selecting a robot action that removes the danger in real time. We use the simulation-based approach to ATM and adopt the 'like-me' policy to assign intentions and actions to people. Using this strategy, the robot can detect and act with a high rate of success under time-constrained situations. The algorithm has been implemented as part of an existing robotics cognitive architecture and tested in simulation scenarios. Three experiments have been conducted to test the implementation's robustness, precision and real-time response, including a simulated scenario, a human-in-the-loop hybrid configuration and a real-world scenario.

翻译：为使机器人实现社会性交互，其必须准确解读人类意图并预测潜在后果。这对于为人类护理而设计的社交机器人尤为重要，此类机器人可能面临对人类具有潜在危险的场景，例如行进路线上不可见的障碍物，这些情况应当予以规避。本文探讨了用于推断和解释人类意图的人工心智理论（ATM）方法。我们提出一种算法，用于检测对人类构成风险的情境，并实时选择机器人动作以消除危险。我们采用基于仿真的ATM方法，并采纳“类我”策略为人类分配意图与动作。运用此策略，机器人能够在时间受限的情况下以高成功率进行检测与行动。该算法已作为现有机器人认知架构的一部分得以实现，并在仿真场景中进行了测试。我们开展了三项实验以检验该实现的鲁棒性、精确性与实时响应能力，包括仿真场景、人在回路混合配置以及真实世界场景。