  Imitating human demonstrations is a promising approach to endow robots with
various manipulation capabilities. While recent advances have been made in
imitation learning and batch (offline) reinforcement learning, a lack of
open-source human datasets and reproducible learning methods make assessing the
state of the field difficult. In this paper, we conduct an extensive study of
six offline learning algorithms for robot manipulation on five simulated and
three real-world multi-stage manipulation tasks of varying complexity, and with
datasets of varying quality. Our study analyzes the most critical challenges
when learning from offline human data for manipulation. Based on the study, we
derive a series of lessons including the sensitivity to different algorithmic
design choices, the dependence on the quality of the demonstrations, and the
variability based on the stopping criteria due to the different objectives in
training and evaluation. We also highlight opportunities for learning from
human datasets, such as the ability to learn proficient policies on
challenging, multi-stage tasks beyond the scope of current reinforcement
learning methods, and the ability to easily scale to natural, real-world
manipulation scenarios where only raw sensory signals are available. We have
open-sourced our datasets and all algorithm implementations to facilitate
future research and fair comparisons in learning from human demonstration data.
Codebase, datasets, trained models, and more available at
https://arise-initiative.github.io/robomimic-web/
