Max Robotics — Global Service Robot Directory + US Certification

About

Yuqi Xie is a leading researcher at the intersection of robotics, imitation learning, and humanoid manipulation. His work focuses on overcoming the critical data bottleneck in dexterous robotics by developing scalable, automated methods for skill acquisition. Xie’s major contributions include pioneering automated data generation for bimanual dexterous manipulation (DexMimicGen, 8 citations) and creating open foundation models for generalist humanoid robots (GR00T N1, 4 citations). He has also advanced vision-based robotic manipulation through sim-and-real co-training strategies and introduced OKAMI, a method that enables humanoid robots to learn manipulation skills from single video demonstrations via object-aware retargeting. With a growing citation impact, Xie’s research is shaping the future of general-purpose robots by reducing reliance on costly human data collection and enabling more efficient, scalable learning from simulation and human video. His work is essential reading for anyone interested in the next generation of autonomous, dexterous robots.