论文标题
在机器人的怀抱中:设计自主拥抱机器人,用跳刺手势
In the Arms of a Robot: Designing Autonomous Hugging Robots with Intra-Hug Gestures
论文作者
论文摘要
拥抱是复杂的情感互动,通常包括挤压等手势。我们介绍了设计六个新指南,以设计互动拥抱机器人,我们通过使用定制机器人进行两项研究来验证它们。为了实现自主权,我们调查了对四个人类弹内手势的机器人反应:握住,摩擦,拍拍和挤压。 32名用户分别通过实验者控制的Huggiebot 2.0交换并评估了16个拥抱。该机器人的充气躯干的麦克风和压力传感器收集了受试者演示的数据,该数据用于开发一种感知算法,该算法对用户行动进行了88 \%的精度分类。用户喜欢机器人挤压,无论他们执行的操作如何,他们都会在机器人响应中重视多样性,并且他们赞赏机器人发射的弹力内手势。从平均用户评分来看,我们创建了一种概率行为算法,该算法可以实时选择机器人响应。我们对机器人平台实施了改进,以创建HuggieBot 3.0,然后用16个用户验证了其手势感知系统和行为算法。机器人的反应和主动手势非常享受。用户在实验的最后阶段发现机器人比第一阶段更自然,愉快和聪明。研究结束后,他们感到机器人更加理解,并认为机器人可以拥抱。
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.