  Imitation learning is a promising paradigm for training robot control
policies, but these policies can suffer from distribution shift, where the
conditions at evaluation time differ from those in the training data. A popular
approach for increasing policy robustness to distribution shift is interactive
imitation learning (i.e., DAgger and variants), where a human operator provides
corrective interventions during policy rollouts. However, collecting a
sufficient amount of interventions to cover the distribution of policy mistakes
can be burdensome for human operators. We propose IntervenGen (I-Gen), a novel
data generation system that can autonomously produce a large set of corrective
interventions with rich coverage of the state space from a small number of
human interventions. We apply I-Gen to 4 simulated environments and 1 physical
environment with object pose estimation error and show that it can increase
policy robustness by up to 39x with only 10 human interventions. Videos and
more results are available at https://sites.google.com/view/intervengen2024.
