  We introduce a novel framework for automatic capturing of human portraits.
The framework allows the robot to follow a person to the desired location using
a Person Re-identification model. When composing is activated, the robot
attempts to adjust its position to form the view that can best match the given
template image, and finally takes a photograph. A template image can be
predicted dynamically using an off-the-shelf photo evaluation model by the
framework, or selected manually from a pre-defined set by the user. The
template matching-based view adjustment is driven by a deep reinforcement
learning network. Our framework lies on top of the Robot Operating System
(ROS). The framework is designed to be modular so that all the models can be
flexibly replaced based on needs. We show our framework on a variety of
examples. In particular, we tested it in three indoor scenes and used it to
take 20 photos of each scene: ten for the pre-defined template, ten for the
dynamically generated ones. The average number of adjustment was $11.20$ for
pre-defined templates and $12.76$ for dynamically generated ones; the average
time spent was $22.11$ and $24.10$ seconds respectively.
