# A Framework for Learning Behavior Trees in Collaborative Robotic Applications

arXiv: https://arxiv.org/abs/2303.11026 · HTML: https://mlsanity.com/paper/2303.11026/

## Key facts

- Cited by 2 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 20 Mar 2023.

## Built on by (most cited first, 2 of 2)

- [A Study on Training and Developing Large Language Models for Behavior Tree Generation](https://mlsanity.com/paper/2401.08089/) (2024)
- [APTree: Extended Behavior Trees for Adaptive and Scalable Automated Robotic Task Planning in Construction](https://mlsanity.com/paper/2610.18523/) (2026)

## Abstract (arXiv)

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.