A novel method for composite facial expressions generation based on multimodal reinforcement learning

Humanoid robots hold significant promise for social interaction and emotional companionship. However, their effectiveness hinges on the ability to convey nuanced and authentic emotions. Here, we presented a universal humanoid robot head with a facial kinematics model. Using a reinforcement learning framework guided by symmetry assessment, emotion decoupling, and MLLM authenticity evaluation, our system autonomously learns to generate adaptive facial expressions through dynamic landmark adjustments. By transferring the simulation training results to real-world environments, the robot can perform natural and expressive expressions. Another novel feature is the independent regulation of emotion intensity and expression magnitude across emotional categories, which enhances the ability to achieve culturally adaptive and socially resonant robotic expressions significantly. This research advances adaptive humanoid interaction, offering an easier and more efficient pathway toward culturally resonant and psychologically plausible robotic expressions.