  This work aims to learn how to perform complex robot manipulation tasks that
are composed of several, consecutively executed low-level sub-tasks, given as
input a few visual demonstrations of the tasks performed by a person. The
sub-tasks consist of moving the robot's end-effector until it reaches a
sub-goal region in the task space, performing an action, and triggering the
next sub-task when a pre-condition is met. Most prior work in this domain has
been concerned with learning only low-level tasks, such as hitting a ball or
reaching an object and grasping it. This paper describes a new neural
network-based framework for learning simultaneously low-level policies as well
as high-level policies, such as deciding which object to pick next or where to
place it relative to other objects in the scene. A key feature of the proposed
approach is that the policies are learned directly from raw videos of task
demonstrations, without any manual annotation or post-processing of the data.
Empirical results on object manipulation tasks with a robotic arm show that the
proposed network can efficiently learn from real visual demonstrations to
perform the tasks, and outperforms popular imitation learning algorithms.
