On the causality between affective impact and coordinated human-robot reactions
Morten Roed Frederiksen, Kasper Støy
- 发表年份
- 2025
- 访问权限
- 开放获取
摘要
In an effort to improve how robots function in social contexts, this paper investigates if a robot that actively shares a reaction to an event with a human alters how the human perceives the robot's affective impact. To verify this, we created two different test setups. One to highlight and isolate the reaction element of affective robot expressions, and one to investigate the effects of applying specific timing delays to a robot reacting to a physical encounter with a human. The first test was conducted with two different groups (n=84) of human observers, a test group and a control group both interacting with the robot. The second test was performed with 110 participants using increasingly longer reaction delays for the robot with every ten participants. The results show a statistically significant change (p$<$.05) in perceived affective impact for the robots when they react to an event shared with a human observer rather than reacting at random. The result also shows for shared physical interaction, the near-human reaction times from the robot are most appropriate for the scenario. The paper concludes that a delay time around 200ms may render the biggest impact on human observers for small-sized non-humanoid robots. It further concludes that a slightly shorter reaction time around 100ms is most effective when the goal is to make the human observers feel they made the biggest impact on the robot.
关键词
相关论文
工业5.0中人机协作的多模态感知、互认知与具身执行综述与展望
Kai Ding, Qingyuan Mao, Yaqian Zhang 等 6 位作者
Robotics and Computer-Integrated Manufacturing · 2026
迈向以人为中心的制造:人机协作装配中不确定性下的任务规划
Yingchao You, Ze Ji, Changyun Wei
Robotics and Computer-Integrated Manufacturing · 2026
代理式人机协作:通过记忆实现上下文对齐
Jiahui Si, Wenchao Li, Xi Chen 等 7 位作者
Robotics and Computer-Integrated Manufacturing · 2026
自适应物理信息Transformer结合高斯过程残差补偿用于人机协作中的逆动力学建模
Rui Qian, Xi Zhang, Dongpeng Li 等 5 位作者
Robotics and Computer-Integrated Manufacturing · 2026