Max Robotics — Global Service Robot Directory + US Certification

About

Hejia Zhang is a robotics and machine learning researcher whose work spans reinforcement learning, simulation-to-real transfer, multi-robot coordination, and scalable robot data collection. With a body of research accumulating over 100 citations, Zhang has made meaningful contributions to some of the most challenging problems at the intersection of robot learning and physical interaction. Zhang's early work tackled the sim-to-real gap—the persistent challenge of training robots in simulation and deploying them reliably in the real world—through innovative approaches such as learning latent skill spaces and task representations for zero-shot generalization. His influential paper on Interactive Differentiable Simulation (35 citations) advanced physics-informed learning for intelligent agents, while subsequent work on multi-robot geometric task-and-motion planning introduced both MIP-based and collaborative frameworks enabling teams of robots to manipulate objects in complex environments. Notably, Zhang explored learning collaborative manipulation strategies directly from YouTube videos, demonstrating a creative approach to scalable robot instruction. More recently, his PATO system addressed the bottleneck of robotic data collection by enabling a single operator to supervise multiple robots simultaneously, a critical step toward large-scale robot learning. His research consistently bridges theoretical rigor with practical deployment, making him a distinctive voice in modern robotics research.