  Living organisms interact with their surroundings in a closed-loop fashion,
where sensory inputs dictate the initiation and termination of behaviours. Even
simple animals are able to develop and execute complex plans, which has not yet
been replicated in robotics using pure closed-loop input control. We propose a
solution to this problem by defining a set of discrete and temporary
closed-loop controllers, called "tasks", each representing a closed-loop
behaviour. We further introduce a supervisory module which has an innate
understanding of physics and causality, through which it can simulate the
execution of task sequences over time and store the results in a model of the
environment. On the basis of this model, plans can be made by chaining
temporary closed-loop controllers. The proposed framework was implemented for a
real robot and tested in two scenarios as proof of concept.
