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.

翻译：模拟学习和一般机器人学习的出现是由于机器学习的突破,而不是机器人学习的突破。 因此,机器人学习的评价指标深深植根于机器学习的指标中,并主要侧重于数据效率。 我们认为,真实世界机器人学习的更好衡量标准是时间效率,这更好地模拟了人类的真正代价。 这是对机器人学习界的号召,要求他们制定我们自己的评价指标,以适应真实世界机器人的长期目标。