标题： 为差异设计：人类特征如何影响对协作机器人的感知 显示英文标题

标题： Designing for Difference: How Human Characteristics Shape Perceptions of Collaborative Robots

摘要： 辅助机器人在社会协作中的发展引发了关于负责任和包容性设计的关键问题，尤其是在与受保护群体（如残疾人或高龄人士）互动时。目前，关于参与者如何评估不同机器人行为与多样化人类需求相结合的研究很少，这可能是因为参与者与先进的家用机器人在现实世界中的经验有限。在当前的研究中，我们旨在填补这一空白，同时使用使参与者能够评估机器人行为以及在经验有限的情况下支持有意义反思的方法。在一个在线研究中，112名参与者（包括实验组和对照组）评估了总共28种人机协作类型中的7个视频。实验组在提供评分之前首先完成了一个人机协作的认知-情感映射（CAM）练习。尽管CAM反思并未显著影响总体评分，但它导致了某些机器人行为与人类状况组合的更明显评估。最重要的是，人机协作的类型会影响评估。反社会的机器人行为始终被评为最低，而与老年人的协作则引发了更敏感的评价。涉及物品交接的场景比不涉及这些场景的评价更为积极。这些发现表明，人类特征和交互范式都会影响对协作机器人的感知接受度，强调了亲社会设计的重要性。它们还突出了反思方法（如CAM）在激发细致反馈方面的潜力，支持为多样化人群量身定制的以用户为中心和社会负责任的机器人系统的发展。 显示英文摘要

摘要： The development of assistive robots for social collaboration raises critical questions about responsible and inclusive design, especially when interacting with individuals from protected groups such as those with disabilities or advanced age. Currently, research is scarce on how participants assess varying robot behaviors in combination with diverse human needs, likely since participants have limited real-world experience with advanced domestic robots. In the current study, we aim to address this gap while using methods that enable participants to assess robot behavior, as well as methods that support meaningful reflection despite limited experience. In an online study, 112 participants (from both experimental and control groups) evaluated 7 videos from a total of 28 variations of human-robot collaboration types. The experimental group first completed a cognitive-affective mapping (CAM) exercise on human-robot collaboration before providing their ratings. Although CAM reflection did not significantly affect overall ratings, it led to more pronounced assessments for certain combinations of robot behavior and human condition. Most importantly, the type of human-robot collaboration influences the assessment. Antisocial robot behavior was consistently rated as the lowest, while collaboration with aged individuals elicited more sensitive evaluations. Scenarios involving object handovers were viewed more positively than those without them. These findings suggest that both human characteristics and interaction paradigms influence the perceived acceptability of collaborative robots, underscoring the importance of prosocial design. They also highlight the potential of reflective methods, such as CAM, to elicit nuanced feedback, supporting the development of user-centered and socially responsible robotic systems tailored to diverse populations.