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arXiv · 2303.11026

A Framework for Learning Behavior Trees in Collaborative Robotic Applications

Matteo Iovino·Jonathan Styrud·Pietro Falco·Christian Smith
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Abstract

In modern industrial collaborative robotic applications, it is desirable to create robot programs automatically, intuitively, and time-efficiently. Moreover, robots need to be controlled by reactive policies to face the unpredictability of the environment they operate in. In this paper we propose a framework that combines a method that learns Behavior Trees (BTs) from demonstration with a method that evolves them with Genetic Programming (GP) for collaborative robotic applications. The main contribution of this paper is to show that by combining the two learning methods we obtain a method that allows non-expert users to semi-automatically, time-efficiently, and interactively generate BTs. We validate the framework with a series of manipulation experiments. The BT is fully learnt in simulation and then transferred to a real collaborative robot.

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Matteo Iovino, Jonathan Styrud, Pietro Falco, Christian Smith. 2023-03-20. A Framework for Learning Behavior Trees in Collaborative Robotic Applications. https://arxiv.org/abs/2303.11026

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