  Deep Reinforcement Learning (DRL) is emerging as a promising approach to
generate adaptive behaviors for robotic platforms. However, a major drawback of
using DRL is the data-hungry training regime that requires millions of trial
and error attempts, which is impractical when running experiments on robotic
systems. Learning from Demonstrations (LfD) has been introduced to solve this
issue by cloning the behavior of expert demonstrations. However, LfD requires a
large number of demonstrations that are difficult to be acquired since
dedicated complex setups are required. To overcome these limitations, we
propose a multi-subtask reinforcement learning methodology where complex pick
and place tasks can be decomposed into low-level subtasks. These subtasks are
parametrized as expert networks and learned via DRL methods. Trained subtasks
are then combined by a high-level choreographer to accomplish the intended pick
and place task considering different initial configurations. As a testbed, we
use a pick and place robotic simulator to demonstrate our methodology and show
that our method outperforms a benchmark methodology based on LfD in terms of
sample-efficiency. We transfer the learned policy to the real robotic system
and demonstrate robust grasping using various geometric-shaped objects.
