  Imitation learning from a large set of human demonstrations has proved to be
an effective paradigm for building capable robot agents. However, the
demonstrations can be extremely costly and time-consuming to collect. We
introduce MimicGen, a system for automatically synthesizing large-scale, rich
datasets from only a small number of human demonstrations by adapting them to
new contexts. We use MimicGen to generate over 50K demonstrations across 18
tasks with diverse scene configurations, object instances, and robot arms from
just ~200 human demonstrations. We show that robot agents can be effectively
trained on this generated dataset by imitation learning to achieve strong
performance in long-horizon and high-precision tasks, such as multi-part
assembly and coffee preparation, across broad initial state distributions. We
further demonstrate that the effectiveness and utility of MimicGen data compare
favorably to collecting additional human demonstrations, making it a powerful
and economical approach towards scaling up robot learning. Datasets, simulation
environments, videos, and more at https://mimicgen.github.io .
