Smile Like You Mean It: Driving Animatronic Robotic Face with Learned Models
Smile Like You Mean It: Driving Animatronic Robotic Face with Learned Models
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DOI：
10.1109/icra48506.2021.9560797
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发表时间：
2021-05
期刊：
影响因子：
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通讯作者：
Boyuan Chen-;Yuhang Hu;Lianfeng Li;S. Cummings;Hod Lipson
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文献类型：
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作者：
Boyuan Chen-;Yuhang Hu;Lianfeng Li;S. Cummings;Hod Lipson
生成智能和可概括的面部表情的能力对于构建类人社交机器人至关重要。目前，该领域的进展受到每个面部表情都需要由人类编程的事实的阻碍。为了使机器人行为在真实的时间适应与人类主体交互时出现的不同情况，机器人需要能够在不需要人类标签的情况下训练自己，以及做出快速的行动决策，并将获得的知识推广到不同的新环境。我们通过设计一个具有柔软皮肤的物理电子动画机器人面部，并开发一个基于视觉的面部模仿自我监督学习框架来解决这一挑战。我们的算法不需要任何知识的机器人的运动学模型，摄像机校准或预定义的表达式集。通过将学习过程分解为生成模型和逆模型，我们的框架可以使用单个运动Babbling数据集进行训练。综合评估表明，我们的方法可以在不同的人类主体中实现准确和多样化的面部模仿。
Ability to generate intelligent and generalizable facial expressions is essential for building human-like social robots. At present, progress in this field is hindered by the fact that each facial expression needs to be programmed by humans. In order to adapt robot behavior in real time to different situations that arise when interacting with human subjects, robots need to be able to train themselves without requiring human labels, as well as make fast action decisions and generalize the acquired knowledge to diverse and new contexts. We addressed this challenge by designing a physical animatronic robotic face with soft skin and by developing a vision-based self-supervised learning framework for facial mimicry. Our algorithm does not require any knowledge of the robot's kinematic model, camera calibration or predefined expression set. By decomposing the learning process into a generative model and an inverse model, our framework can be trained using a single motor babbling dataset. Comprehensive evaluations show that our method enables accurate and diverse face mimicry across diverse human subjects.