Learning Behavior Trees with Genetic Programming in Unpredictable\n Environments

2020/11/06 by Matteo Iovino, Jonathan Styrud, Iovino, Matteo +5 · 2 citations
Computer Science · #Artificial Intelligence (cs.AI) #Evolutionary Algorithms and Applications #FOS: Computer and information sciences #Machine Learning and Data Classification #Reinforcement Learning in Robotics #Robotics (cs.RO)

paper · pdf · doi:10.48550/arxiv.2011.03252

openalex publication_date 2020/11/06 · openalex created_date 2025/10/10 · openalex updated_date 2026/07/28

Abstract

Modern industrial applications require robots to be able to operate in\nunpredictable environments, and programs to be created with a minimal effort,\nas there may be frequent changes to the task. In this paper, we show that\ngenetic programming can be effectively used to learn the structure of a\nbehavior tree (BT) to solve a robotic task in an unpredictable environment.\nMoreover, we propose to use a simple simulator for the learning and demonstrate\nthat the learned BTs can solve the same task in a realistic simulator, reaching\nconvergence without the need for task specific heuristics. The learned solution\nis tolerant to faults, making our method appealing for real robotic\napplications.\n

Cited by

• Learning Stack-of-Tasks Management for Redundant Robots
• Adversarial Coevolutionary Illumination with Generational Adversarial MAP-Elites

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