Researchers developed SimHum, a co-training recipe that leverages simulation and human demonstrations to improve data efficiency and scene generalizability in bimanual manipulation tasks. This approach extracts kinematic priors from simulation and visual priors from human observations, then fine-tunes on a small real-robot dataset. The result is a 53.7% increase in absolute success rate compared to using only real-world data and a 35.0% improvement over the best single-source pre-training baseline.