Max Robotics — Global Service Robot Directory + US Certification

About

Chongjie Zhang is a leading researcher at the intersection of robotics, reinforcement learning, and human-robot collaboration. His work fundamentally advances how autonomous systems plan, learn, and coordinate—both among themselves and with humans. A central theme is the co-optimization of task and motion planning, where he has shown that integrating high-level task reasoning with low-level motion constraints yields substantially better solutions for complex robotic manipulation. In reinforcement learning, Zhang has made pivotal contributions to transfer learning, developing the first optimal online method for selecting source policies—a theoretical breakthrough that significantly accelerates learning by exploiting prior knowledge. His work on goal-conditioned supervised learning has also reshaped offline RL, offering a simpler, more stable alternative to traditional algorithms. Beyond single-agent systems, Zhang is a pioneer in multi-agent learning and human-robot teaming. He introduced perturbation training, a computational model enabling human-robot teams to co-develop joint strategies, and developed Bayesian inference approaches to model trust dynamics in these teams. With papers accumulating over 100 citations and appearing at top venues like RSS, NeurIPS, and ICRA, Zhang’s research is shaping the next generation of intelligent, collaborative robots.