  End-to-end control for robot manipulation and grasping is emerging as an
attractive alternative to traditional pipelined approaches. However, end-to-end
methods tend to either be slow to train, exhibit little or no generalisability,
or lack the ability to accomplish long-horizon or multi-stage tasks. In this
paper, we show how two simple techniques can lead to end-to-end (image to
velocity) execution of a multi-stage task, which is analogous to a simple
tidying routine, without having seen a single real image. This involves
locating, reaching for, and grasping a cube, then locating a basket and
dropping the cube inside. To achieve this, robot trajectories are computed in a
simulator, to collect a series of control velocities which accomplish the task.
Then, a CNN is trained to map observed images to velocities, using domain
randomisation to enable generalisation to real world images. Results show that
we are able to successfully accomplish the task in the real world with the
ability to generalise to novel environments, including those with dynamic
lighting conditions, distractor objects, and moving objects, including the
basket itself. We believe our approach to be simple, highly scalable, and
capable of learning long-horizon tasks that have until now not been shown with
the state-of-the-art in end-to-end robot control.
