Rao Fu
Brown University Research Assistant
rao_fu@brown.edu
Bio
Rao Fu is a PhD student in Computer Science at Brown University, advised by Prof. Srinath Sridhar and Prof. Daniel Ritchie. Her research sits at the intersection of 3D vision, computer graphics, and embodied AI, aming to enable machines to imagine and interact with the world in more human-like ways. She interned at Microsoft Research Asia, Autodesk AI Research, and the Meta LLaMA team, and spent a wonderful time as a visiting researcher at MPI Informatics with Prof. Christian Theobalt and Dr. Rishabh Dabral. Before Brown, she earned her B.Eng. from the University of the Chinese Academy of Sciences, advised by Prof. Lin Gao and Prof. Xilin Chen, and enjoyed a research stay at UC San Diego with Prof. Hao Su.
Areas of Research
- Computer Graphics and Vision
From Words to Worlds: Bridging Physical Intelligence and Linguistic Intelligence through Generative Methods
My research aims to equip machines with physical intelligence, empowering them to assist humans effectively in the real world. Unlike linguistic intelligence, where AI has excelled, physical intelligence addresses perceiving, interpreting, and acting within high-dimensional physical environments, involving understanding complex spatial signals and executing dexterous, embodied action. This direction opens new possibilities in rehabilitation, creative design, augmented reality, and assistive robotics. My research targets the fundamental challenges, including large-scale physical interaction data collection, efficient high-dimensional data representation, and the interoperability of physical phenomena that resist simple linguistic description. Central to my approach is the integration of physical and linguistic intelligence through generative models, organizing low-level signals into human-intuitive, semantic hierarchies. To this end, I explore three interconnected domains, object geometry generation, dexterous motion generation, and house-scale scene generation, using text-conditioned generative tasks as testbeds.