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

Acceleration of Actor-Critic Deep Reinforcement Learning for Visual  Grasping in Clutter by State Representation Learning Based on Disentanglement  of a Raw Input Image