  Behavior Trees (BTs) were first conceived in the computer games industry as a
tool to model agent behavior, but they received interest also in the robotics
community as an alternative policy design to Finite State Machines (FSMs). The
advantages of BTs over FSMs had been highlighted in many works, but there is no
thorough practical comparison of the two designs. Such a comparison is
particularly relevant in the robotic industry, where FSMs have been the
state-of-the-art policy representation for robot control for many years. In
this work we shed light on this matter by comparing how BTs and FSMs behave
when controlling a robot in a mobile manipulation task. The comparison is made
in terms of reactivity, modularity, readability, and design. We propose metrics
for each of these properties, being aware that while some are tangible and
objective, others are more subjective and implementation dependent. The
practical comparison is performed in a simulation environment with validation
on a real robot. We find that although the robot's behavior during task solving
is independent on the policy representation, maintaining a BT rather than an
FSM becomes easier as the task increases in complexity.
