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

TL;DR

This paper compares the programming effort of Behavior Trees and Finite State Machines in robotic tasks, demonstrating that BTs offer lower effort due to their modularity, with validation through simulation experiments.

Contribution

It provides a practical comparison showing that Behavior Trees reduce programming effort compared to FSMs in robotic applications.

Findings

Behavior Trees decrease programming effort due to modularity.

BTs are more reactive and human-readable than FSMs.

Experimental validation in simulation confirms lower modification costs for BTs.

Abstract

In this paper we provide a practical demonstration of how the modularity in a Behavior Tree (BT) decreases the effort in programming a robot task when compared to a Finite State Machine (FSM). In recent years the way to represent a task plan to control an autonomous agent has been shifting from the standard FSM towards BTs. Many works in the literature have highlighted and proven the benefits of such design compared to standard approaches, especially in terms of modularity, reactivity and human readability. However, these works have often failed in providing a tangible comparison in the implementation of those policies and the programming effort required to modify them. This is a relevant aspect in many robotic applications, where the design choice is dictated both by the robustness of the policy and by the time required to program it. In this work, we compare backward chained BTs with a fault-tolerant design of FSMs by evaluating the cost to modify them. We validate the analysis with a set of experiments in a simulation environment where a mobile manipulator solves an item fetching task.