  Large, richly annotated datasets have accelerated progress in fields such as
computer vision and natural language processing, but replicating these
successes in robotics has been challenging. While prior data collection
methodologies such as self-supervision have resulted in large datasets, the
data can have poor signal-to-noise ratio. By contrast, previous efforts to
collect task demonstrations with humans provide better quality data, but they
cannot reach the same data magnitude. Furthermore, neither approach places
guarantees on the diversity of the data collected, in terms of solution
strategies. In this work, we leverage and extend the RoboTurk platform to scale
up data collection for robotic manipulation using remote teleoperation. The
primary motivation for our platform is two-fold: (1) to address the
shortcomings of prior work and increase the total quantity of manipulation data
collected through human supervision by an order of magnitude without
sacrificing the quality of the data and (2) to collect data on challenging
manipulation tasks across several operators and observe a diverse set of
emergent behaviors and solutions. We collected over 111 hours of robot
manipulation data across 54 users and 3 challenging manipulation tasks in 1
week, resulting in the largest robot dataset collected via remote
teleoperation. We evaluate the quality of our platform, the diversity of
demonstrations in our dataset, and the utility of our dataset via quantitative
and qualitative analysis. For additional results, supplementary videos, and to
download our dataset, visit http://roboturk.stanford.edu/realrobotdataset .
