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HATSUKI : An anime character like robot figure platform with anime-style expressions and imitation learning based action generation

机译:HATSUKI:动漫人物,如机器人模型平台,具有动漫风格的表达和基于模仿学习的动作生成

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Japanese character figurines are popular and have a pivot position in Otaku culture. Although numerous robots have been developed, few have focused on otaku-culture or on embodying anime character figurines. Therefore, we take the first steps to bridge this gap by developing Hatsuki, which is a humanoid robot platform with anime based design. Hatsuki’s novelty lies in its aesthetic design, 2D facial expressions, and anime-style behaviors that allows Hatsuki to deliver rich interaction experiences resembling anime-characters. We explain our design implementation process of Hatsuki, followed by our evaluations. In order to explore user impressions and opinions towards Hatsuki, we conducted a questionnaire in the world’s largest anime-figurine event. The results indicate that participants were generally very satisfied with Hatsuki’s design, and proposed various use case scenarios and deployment contexts for Hatsuki. The second evaluation focused on imitation learning, as such a method can provide better interaction ability in the real world and generate rich, context-adaptive behaviors in different situations. We made Hatsuki learn 11 actions, combining voice, facial expressions and motions, through the neural network based policy model with our proposed interface. Results show our approach was successfully able to generate the actions through self-organized contexts, which shows the potential for generalizing our approach in further actions under different contexts. Lastly, we present our future research direction for Hatsuki and provide our conclusion.
机译:日语人物小雕像很受欢迎,在宅男文化中占有举足轻重的地位。尽管已经开发了许多机器人,但很少有人专注于宅男文化或体现动漫人物雕像。因此,我们采取了第一步,通过开发Hatsuki来弥合这种差距,Hatsuki是一种具有动漫设计的类人机器人平台。 Hatsuki的新颖之处在于其美学设计,2D面部表情和动漫风格的行为,使Hatsuki可以提供类似于动漫人物的丰富互动体验。我们解释Hatsuki的设计实现过程,然后进行评估。为了探索用户对初音的印象和意见,我们在全球最大的动漫人物大事件中进行了问卷调查。结果表明,参与者通常对Hatsuki的设计非常满意,并为Hatsuki提出了各种用例场景和部署上下文。第二项评估侧重于模仿学习,因为这种方法可以在现实世界中提供更好的交互能力,并在不同情况下产生丰富的,适应上下文的行为。通过基于神经网络的策略模型和我们提出的界面,我们让Hatsuki学习了11种动作,将语音,面部表情和动作结合在一起。结果表明,我们的方法能够成功地通过自组织上下文生成行动,这表明了在不同上下文中进一步采取行动时推广我们的方法的潜力。最后,我们介绍了Hatsuki的未来研究方向并提供了结论。

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