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：为使机器人能够进行社交互动，必须准确解读人类意图并预测其潜在后果。这对于设计用于人类护理的社交机器人尤为重要，因为这类机器人可能面临需避免的人类潜在危险情境（例如行人在路径中无法察觉的障碍物）。本文探索基于人工心理理论（ATM）的方法来推断和解读人类意图。我们提出一种算法，用于检测人类面临的危险情境，并实时选择消除危险的机器人动作。我们采用基于仿真的ATM方法，并遵循"像我们一样"策略为人类赋予意图和动作。通过该策略，机器人能在时间受限情境下以高成功率进行检测和行动。该算法已作为现有机器人认知架构的一部分实现，并在仿真场景中进行了测试。我们开展了三项实验验证实现的鲁棒性、精度及实时响应能力，包括仿真场景、人在回路混合配置以及真实世界场景。