Paul Schydlo

Papers

Total Citations

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About

Paul Schydlo is a robotics researcher whose work sits at the intersection of socially assistive robotics and soft robotic control. His primary research areas include human-robot interaction for therapeutic applications, particularly for individuals with autism spectrum disorder (ASD), and machine learning approaches for modeling complex robotic systems. In his most impactful work, Schydlo proposed a novel robotic coaching platform designed to train motor, social, and cognitive skills in children with ASD through structured, dyadic interaction games—a significant step beyond the simplified, qualitative protocols that previously dominated the field. This paper has garnered 34 citations, reflecting its influence on the development of quantitative, technology-driven therapeutic interventions. In parallel, Schydlo has advanced the modeling of soft robotic hands, introducing a "Sensorimotor Graph" framework that uses action-conditioned graph neural networks to learn and predict the complex, non-rigid dynamics of these bio-inspired systems. This work addresses a core challenge in soft robotics: achieving precise control over flexible, difficult-to-model materials. By bridging the gap between adaptive hardware and intelligent control, Schydlo’s research demonstrates a dual commitment to both human-centered robotics and fundamental robotic learning.