  Imitation Learning has empowered recent advances in learning robotic
manipulation tasks by addressing shortcomings of Reinforcement Learning such as
exploration and reward specification. However, research in this area has been
limited to modest-sized datasets due to the difficulty of collecting large
quantities of task demonstrations through existing mechanisms. This work
introduces RoboTurk to address this challenge. RoboTurk is a crowdsourcing
platform for high quality 6-DoF trajectory based teleoperation through the use
of widely available mobile devices (e.g. iPhone). We evaluate RoboTurk on three
manipulation tasks of varying timescales (15-120s) and observe that our user
interface is statistically similar to special purpose hardware such as virtual
reality controllers in terms of task completion times. Furthermore, we observe
that poor network conditions, such as low bandwidth and high delay links, do
not substantially affect the remote users' ability to perform task
demonstrations successfully on RoboTurk. Lastly, we demonstrate the efficacy of
RoboTurk through the collection of a pilot dataset; using RoboTurk, we
collected 137.5 hours of manipulation data from remote workers, amounting to
over 2200 successful task demonstrations in 22 hours of total system usage. We
show that the data obtained through RoboTurk enables policy learning on
multi-step manipulation tasks with sparse rewards and that using larger
quantities of demonstrations during policy learning provides benefits in terms
of both learning consistency and final performance. For additional results,
videos, and to download our pilot dataset, visit
$\href{http://roboturk.stanford.edu/}{\texttt{roboturk.stanford.edu}}$
