Hugs are complex affective interactions that often include gestures like squeezes. We present six new guidelines for designing interactive hugging robots, which we validate through two studies with our custom robot. To achieve autonomy, we investigated robot responses to four human intra-hug gestures: holding, rubbing, patting, and squeezing. Thirty-two users each exchanged and rated sixteen hugs with an experimenter-controlled HuggieBot 2.0. The robot's inflated torso's microphone and pressure sensor collected data of the subjects' demonstrations that were used to develop a perceptual algorithm that classifies user actions with 88\% accuracy. Users enjoyed robot squeezes, regardless of their performed action, they valued variety in the robot response, and they appreciated robot-initiated intra-hug gestures. From average user ratings, we created a probabilistic behavior algorithm that chooses robot responses in real time. We implemented improvements to the robot platform to create HuggieBot 3.0 and then validated its gesture perception system and behavior algorithm with sixteen users. The robot's responses and proactive gestures were greatly enjoyed. Users found the robot more natural, enjoyable, and intelligent in the last phase of the experiment than in the first. After the study, they felt more understood by the robot and thought robots were nicer to hug.
翻译:拥抱是复杂的情感互动, 通常包括挤压等手势。 我们提出设计交互式拥抱机器人的六种新准则, 我们通过与自定义机器人进行的两项研究来验证。 为了实现自主, 我们调查机器人对四种人类内拥抱的手势的反应: 持有、 擦擦、 拍拍和挤压。 三十二个用户每人交换和评分十六个拥抱, 由实验者控制的 HuggieBot 2.0 。 机器人膨胀的托尔索麦克风和压力感应器收集了实验对象演示的数据, 这些数据用来发展一种概念性算法, 将用户的行为精确地分类 88 。 用户享受机器人的挤压, 不论他们的行为如何, 他们重视机器人的反应的多样性, 他们欣赏机器人内部的手势。 从普通用户的评分来看, 我们创造了一种概率性的行为演算法, 实时选择机器人的反应。 我们对机器人平台进行了改进, 以创建 HuggieBot 3.0, 然后与十六个用户验证其姿态感知和行为算法的数据。 机器人的反应和主动的动作手法得到了很好的享受。 机器人的反应和动作在机器人的最后一个阶段中, 使用者们发现, 更了解了更自然阶段后, 在机器人的实验研究中发现, 更自然阶段中, 更理解和机械机器人实验中, 更感知更感知更感知更感知更感, 更感知, 更感更感知, 更感知和更感知, 更感知, 更感知于机器人更感知, 。