  Grasping objects in cluttered scenarios is a challenging task in robotics.
Performing pre-grasp actions such as pushing and shifting to scatter objects is
a way to reduce clutter. Based on deep reinforcement learning, we propose a
Fast-Learning Grasping (FLG) framework, that can integrate pre-grasping actions
along with grasping to pick up objects from cluttered scenarios with reduced
real-world training time. We associate rewards for performing moving actions
with the change of environmental clutter and utilize a hybrid triggering
method, leading to data-efficient learning and synergy. Then we use the output
of an extended fully convolutional network as the value function of each pixel
point of the workspace and establish an accurate estimation of the grasp
probability for each action. We also introduce a mask function as prior
knowledge to enable the agents to focus on the accurate pose adjustment to
improve the effectiveness of collecting training data and, hence, to learn
efficiently. We carry out pre-training of the FLG over simulated environment,
and then the learnt model is transferred to the real world with minimal
fine-tuning for further learning during actions. Experimental results
demonstrate a 94% grasp success rate and the ability to generalize to novel
objects. Compared to state-of-the-art approaches in the literature, the
proposed FLG framework can achieve similar or higher grasp success rate with
lesser amount of training in the real world. Supplementary video is available
at https://youtu.be/e04uDLsxfDg.
