Jingbo Wang is a lead researcher in humanoid robot learning at the Embodied AI Center of Shanghai AI Lab. His research focuses on enabling humanoid robots to acquire versatile motor and cognitive skills and to autonomously adapt to complex, dynamic environments. His work aims to bridge the gap between artificial agents and human-level autonomy. Before his current role, Jingbo conducted research across several domains in computer vision and graphics, including character animation and simulation, efficient perception modeling, and 3D reconstruction. He played a key role in the development of widely used real-time semantic segmentation models, notably BiSeNet V1 and V2.
