IntervenGen: Interventional Data Generation for Robust and Data-Efficient Robot Imitation Learning

TL;DR

IntervenGen is a system that autonomously generates extensive corrective data for robot imitation learning, significantly improving policy robustness with minimal human intervention.

Contribution

It introduces IntervenGen, a novel autonomous data generation method that enhances robot policy robustness efficiently from limited human interventions.

Findings

Increased policy robustness by up to 39x

Achieved effective data coverage with only 10 human interventions

Validated across multiple simulated and physical environments

Abstract

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.