Max Robotics — Global Service Robot Directory + US Certification

About

Sifan Song is a leading researcher at the intersection of social robotics, human-robot interaction (HRI), and assistive technology for neurodevelopmental disorders. Their work focuses on enabling robots to perceive, interpret, and respond to human social cues—including intention, emotion, and attention—to create more natural and effective interactions. Song’s most impactful contribution is the development of a visual-NLP semantic framework for intention understanding in HRI (42 citations), which allows robots to interpret verbal commands alongside visual context. They also pioneered FECTS, a facial emotion cognition and training system for Chinese children with Autism Spectrum Disorder (28 citations), and an attention-based interactive system that uses gaze-following to support autistic children’s communication. Their research extends to tactile perception for whole-body social interaction and bionic motion learning for quadruped robots. With a portfolio spanning image captioning in Chinese, low-cost tactile systems, and animal-inspired locomotion, Song’s work has practical implications for rehabilitation robotics and inclusive technology. Their contributions are shaping how socially assistive robots can better understand and support individuals with cognitive and communication challenges.