  We present a large-scale study of imitating human demonstrations on tasks
that require a virtual robot to search for objects in new environments -- (1)
ObjectGoal Navigation (e.g. 'find & go to a chair') and (2) Pick&Place (e.g.
'find mug, pick mug, find counter, place mug on counter'). First, we develop a
virtual teleoperation data-collection infrastructure -- connecting Habitat
simulator running in a web browser to Amazon Mechanical Turk, allowing remote
users to teleoperate virtual robots, safely and at scale. We collect 80k
demonstrations for ObjectNav and 12k demonstrations for Pick&Place, which is an
order of magnitude larger than existing human demonstration datasets in
simulation or on real robots.
  Second, we attempt to answer the question -- how does large-scale imitation
learning (IL) (which hasn't been hitherto possible) compare to reinforcement
learning (RL) (which is the status quo)? On ObjectNav, we find that IL (with no
bells or whistles) using 70k human demonstrations outperforms RL using 240k
agent-gathered trajectories. The IL-trained agent demonstrates efficient
object-search behavior -- it peeks into rooms, checks corners for small
objects, turns in place to get a panoramic view -- none of these are exhibited
as prominently by the RL agent, and to induce these behaviors via RL would
require tedious reward engineering. Finally, accuracy vs. training data size
plots show promising scaling behavior, suggesting that simply collecting more
demonstrations is likely to advance the state of art further. On Pick&Place,
the comparison is starker -- IL agents achieve ${\sim}$18% success on episodes
with new object-receptacle locations when trained with 9.5k human
demonstrations, while RL agents fail to get beyond 0%. Overall, our work
provides compelling evidence for investing in large-scale imitation learning.
  Project page: https://ram81.github.io/projects/habitat-web.
