2022/09/15 by Matteo Iovino, Julian Förster, Iovino, Matteo +9 · 3 citations
Computer Science · Engineering · #Artificial intelligence #Computer science #Distributed computing #Engineering #Evolutionary Algorithms and Applications #FOS: Computer and information sciences #Fault tolerance #Finite-state machine #Machine Learning and Algorithms #Modularity (biology) #Programming language #Reinforcement Learning in Robotics #Robot #Robotics (cs.RO) #Robustness (evolution) #Set (abstract data type) #Software engineering #Systems engineering #Task (project management) #cs.RO
paper · pdf · doi:10.48550/arxiv.2209.07392
published in arXiv (Cornell University) (Cornell University) · Submitted to 2023 IEEE International Conference on Robotics and Automation (ICRA)
arxiv created 2022/09/15 · openalex publication_date 2022/09/15 · arxiv updated 2022/09/16 · openalex created_date 2022/10/01 · openalex updated_date 2026/08/05
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.