  For a robotic grasping task in which diverse unseen target objects exist in a
cluttered environment, some deep learning-based methods have achieved
state-of-the-art results using visual input directly. In contrast, actor-critic
deep reinforcement learning (RL) methods typically perform very poorly when
grasping diverse objects, especially when learning from raw images and sparse
rewards. To make these RL techniques feasible for vision-based grasping tasks,
we employ state representation learning (SRL), where we encode essential
information first for subsequent use in RL. However, typical representation
learning procedures are unsuitable for extracting pertinent information for
learning the grasping skill, because the visual inputs for representation
learning, where a robot attempts to grasp a target object in clutter, are
extremely complex. We found that preprocessing based on the disentanglement of
a raw input image is the key to effectively capturing a compact representation.
This enables deep RL to learn robotic grasping skills from highly varied and
diverse visual inputs. We demonstrate the effectiveness of this approach with
varying levels of disentanglement in a realistic simulated environment.
