  Recently, end-to-end learning frameworks are gaining prevalence in the field
of robot control. These frameworks input states/images and directly predict the
torques or the action parameters. However, these approaches are often critiqued
due to their huge data requirements for learning a task. The argument of the
difficulty in scalability to multiple tasks is well founded, since training
these tasks often require hundreds or thousands of examples. But do end-to-end
approaches need to learn a unique model for every task? Intuitively, it seems
that sharing across tasks should help since all tasks require some common
understanding of the environment. In this paper, we attempt to take the next
step in data-driven end-to-end learning frameworks: move from the realm of
task-specific models to joint learning of multiple robot tasks. In an
astonishing result we show that models with multi-task learning tend to perform
better than task-specific models trained with same amounts of data. For
example, a deep-network learned with 2.5K grasp and 2.5K push examples performs
better on grasping than a network trained on 5K grasp examples.
