  Existing architectures for imitation learning using image-to-action policy
networks perform poorly when presented with an input image containing multiple
instances of the object of interest, especially when the number of expert
demonstrations available for training are limited. We show that end-to-end
policy networks can be trained in a sample efficient manner by (a) appending
the feature map output of the vision layers with an embedding that can indicate
instance preference or take advantage of an implicit preference present in the
expert demonstrations, and (b) employing an autoregressive action generator
network for the control layers. The proposed architecture for localization has
improved accuracy and sample efficiency and can generalize to the presence of
more instances of objects than seen during training. When used for end-to-end
imitation learning to perform reach, push, and pick-and-place tasks on a real
robot, training is achieved with as few as 15 expert demonstrations.
