  We propose a vision-based architecture search algorithm for robot
manipulation learning, which discovers interactions between low dimension
action inputs and high dimensional visual inputs. Our approach automatically
designs architectures while training on the task - discovering novel ways of
combining and attending image feature representations with actions as well as
features from previous layers. The obtained new architectures demonstrate
better task success rates, in some cases with a large margin, compared to a
recent high performing baseline. Our real robot experiments also confirm that
it improves grasping performance by 6%. This is the first approach to
demonstrate a successful neural architecture search and attention connectivity
search for a real-robot task.
