  This paper introduces an end-to-end fine-tuning method to improve hand-eye
coordination in modular deep visuo-motor policies (modular networks) where each
module is trained independently. Benefiting from weighted losses, the
fine-tuning method significantly improves the performance of the policies for a
robotic planar reaching task.
