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Ability to generate intelligent and generalizable facial expressions isessential for building human-like social robots. At present, progress in thisfield is hindered by the fact that each facial expression needs to beprogrammed by humans. In order to adapt robot behavior in real time todifferent situations that arise when interacting with human subjects, robotsneed to be able to train themselves without requiring human labels, as well asmake fast action decisions and generalize the acquired knowledge to diverse andnew contexts. We addressed this challenge by designing a physical animatronicrobotic face with soft skin and by developing a vision-based self-supervisedlearning framework for facial mimicry. Our algorithm does not require anyknowledge of the robot's kinematic model, camera calibration or predefinedexpression set. By decomposing the learning process into a generative model andan inverse model, our framework can be trained using a single motor babblingdataset. Comprehensive evaluations show that our method enables accurate anddiverse face mimicry across diverse human subjects. The project website is athttp://www.cs.columbia.edu/~bchen/aiface/

Smile Like You Mean It: Driving Animatronic Robotic Face with Learned Models