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.