Back to Reality for Imitation Learning

Edward Johns

Keywords: Imitation Learning, Reinforcement Learning, Evaluation, Benchmarks

TL;DR: Evaluation metrics for imitation learning focus too much on data efficiency, and not enough on real-world time efficiency. Let's change that.

Abstract: Imitation learning, and robot learning in general, emerged due to breakthroughs in machine learning, rather than breakthroughs in robotics. As such, evaluation metrics for robot learning are deeply rooted in those for machine learning, and focus primarily on data efficiency. We believe that a better metric for real-world robot learning is time efficiency, which better models the true cost to humans. This is a call to arms to the robot learning community to develop our own evaluation metrics, tailored towards the long-term goals of real-world robotics.