  Traditional AI-planning methods for task planning in robotics require a
symbolically encoded domain description. While powerful in well-defined
scenarios, as well as human-interpretable, setting this up requires substantial
effort. Different from this, most everyday planning tasks are solved by humans
intuitively, using mental imagery of the different planning steps. Here we
suggest that the same approach can be used for robots, too, in cases which
require only limited execution accuracy. In the current study, we propose a
novel sub-symbolic method called Simulated Mental Imagery for Planning (SiMIP),
which consists of perception, simulated action, success-checking and
re-planning performed on 'imagined' images. We show that it is possible to
implement mental imagery-based planning in an algorithmically sound way by
combining regular convolutional neural networks and generative adversarial
networks. With this method, the robot acquires the capability to use the
initially existing scene to generate action plans without symbolic domain
descriptions, while at the same time plans remain human-interpretable,
different from deep reinforcement learning, which is an alternative
sub-symbolic approach. We create a dataset from real scenes for a packing
problem of having to correctly place different objects into different target
slots. This way efficiency and success rate of this algorithm could be
quantified.
