  Recent research demonstrated that it is feasible to end-to-end train
multi-task deep visuomotor policies for robotic manipulation using variations
of learning from demonstration (LfD) and reinforcement learning (RL). In this
paper, we extend the capabilities of end-to-end LfD architectures to object
manipulation in clutter. We start by introducing a data augmentation procedure
called Accept Synthetic Objects as Real (ASOR). Using ASOR we develop two
network architectures: implicit attention ASOR-IA and explicit attention
ASOR-EA. Both architectures use the same training data (demonstrations in
uncluttered environments) as previous approaches. Experimental results show
that ASOR-IA and ASOR-EA succeed ina significant fraction of trials in
cluttered environments where previous approaches never succeed. In addition, we
find that both ASOR-IA and ASOR-EA outperform previous approaches even in
uncluttered environments, with ASOR-EA performing better even in clutter
compared to the previous best baseline in an uncluttered environment.
