# Guessing Human Intentions to Avoid Dangerous Situations in Caregiving Robots
## 1 Introduction  
In recent decades, robotics have advanced by leaps and bounds, opening up new possibilities in various areas, including human care and assistance. Assistive robots, or \"caregiving robots\", have emerged as a promising solution to address the challenges associated with an aging population and caregiver shortages. These robots are designed to help the elderly, disabled, or chronically ill by providing physical, emotional, and social support. To facilitate human-robot collaboration, these robots must be sophisticated in their social environment, demonstrating advanced reactivity, interaction, and comprehension [1]. The development of social awareness in robots is a complex process influenced by many factors, including cultural norms, social cues, diverse human activities, and the individual preferences of those in their vicinity. To achieve social adeptness, robots must go beyond the mere perception of their physical surroundings. They must develop the ability tocomprehend any situation's social and contextual nuances. This involves, among other things, recognizing the presence of people in the robot's surroundings and interpreting their behaviors, emotions, and intentions within the broader social context [2]. Caregiving robots, in particular, face unique challenges that demand an even higher level of social intelligence. These robots must be able to continuously sense and interpret the ongoing activities of individuals within their operational environment. This real-time analysis serves a crucial purpose: anticipating potential risk scenarios before they materialize. Armed with this foresight, caregiving robots must be able to adjust their behavior swiftly and effectively to mitigate emerging risks in a manner that is effective and socially acceptable. The achievement of these goals requires the integration of several advanced capabilities. Firstly, caregiving robots must have predictive algorithms to forecast forthcoming events. Secondly, they need sufficient computational power to analyze these predictions rapidly and foresee their potential consequences across various scenarios. Finally, and perhaps most challenging, these robots must be programmed with an understanding of human social norms and expectations. This understanding should guide the robot's decision-making process, allowing it to select actions that effectively address the immediate situation and align with societal expectations of appropriate behavior. In this paper, we expand upon previous research in the field of artificial theory of mind (ATM) [3] and its relationship to simulation-based internal models [4; 5]. Theory of mind (TM) is a fundamental cognitive capability that enables individuals to predict their actions and those of others [6]. This concept provides a plausible framework for understanding how humans can anticipate the behavior of others in specific situations [4; 7]. Within the broader field of TM research, several theoretical approaches have been proposed to elucidate the underlying cognitive processes. In this study, we adopt the simulation theory (ST) variant, which posits that \"...people use imagination, mental pretense, or perspective-taking ('putting oneself in the other person's shoes') to determine the mental states of others' [8]. This approach emphasizes the role of mental simulation in understanding and predicting the thoughts, feelings, and actions of others. Our primary contribution in this paper is twofold: (i) We aim to further support the hypothesis that simulation-based internal models can serve as a viable foundation for implementing ATM in artificial systems. By demonstrating the efficacy of this approach, we seek to bridge the gap between human cognitive processes and artificial intelligence systems; (ii) Building upon this theoretical framework, we present a working model and a practical example of human caregiving in social robots.