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In this paper we provide a practical demonstration of how the modularity in aBehavior Tree (BT) decreases the effort in programming a robot task whencompared to a Finite State Machine (FSM). In recent years the way to representa task plan to control an autonomous agent has been shifting from the standardFSM towards BTs. Many works in the literature have highlighted and proven thebenefits of such design compared to standard approaches, especially in terms ofmodularity, reactivity and human readability. However, these works have oftenfailed in providing a tangible comparison in the implementation of thosepolicies and the programming effort required to modify them. This is a relevantaspect in many robotic applications, where the design choice is dictated bothby the robustness of the policy and by the time required to program it. In thiswork, we compare backward chained BTs with a fault-tolerant design of FSMs byevaluating the cost to modify them. We validate the analysis with a set ofexperiments in a simulation environment where a mobile manipulator solves anitem fetching task.

On the programming effort required to generate Behavior Trees and Finite State Machines for robotic applications