Shaping Imbalance into Balance: Active Robot Guidance of Human Teachers for Better Learning from Demonstrations

Abstract

Learning from Demonstrations (LfD) transfers skills from human teachers to robots. However, data imbalance in demonstrations can bias policies towards majority situations. Previous work attempted to solve this problem after data collection, but few efforts were made to maintain a balanced distribution from the phase of data acquisition. Our method accounts for the influence of robots on human teachers and enables robots to actively guide interaction to approximate demonstration distributions to target distributions. Simulated and real-world experiments validated the method’s efficacy in shaping demonstration distribution into various target distributions and robustness to various levels of uncertainties. Also, our method significantly improved the generalization ability of robot learning when LfD policies were trained with data collected by our method compared to natural data collection.