# Combining Planning and Learning of Behavior Trees for Robotic Assembly

arXiv: https://arxiv.org/abs/2103.09036 · HTML: https://mlsanity.com/paper/2103.09036/

## Key facts

- Cited by 9 papers in the map (data through 14 Oct 2026).
- Builds on 8 of its 26 references that are in the map.
- First posted on arXiv 16 Mar 2021.

## Builds on (most cited first, 8 of 8)

- [Behavior Trees in Robotics and AI: An Introduction](https://mlsanity.com/paper/1709.00084/) (2017)
- [PRM-RL: Long-range Robotic Navigation Tasks by Combining Reinforcement Learning and Sampling-based Planning](https://mlsanity.com/paper/1710.03937/) (2017)
- [A Survey of Behavior Trees in Robotics and AI](https://mlsanity.com/paper/2005.05842/) (2020)
- [Combined Reinforcement Learning via Abstract Representations](https://mlsanity.com/paper/1809.04506/) (2018)
- [Towards Blended Reactive Planning and Acting using Behavior Trees](https://mlsanity.com/paper/1611.00230/) (2016)
- [Learning of Behavior Trees for Autonomous Agents](https://mlsanity.com/paper/1504.05811/) (2015)
- [Learning Behavior Trees with Genetic Programming in Unpredictable Environments](https://mlsanity.com/paper/2011.03252/) (2020)
- [2019 Evolutionary Algorithms Review](https://mlsanity.com/paper/1906.08870/) (2019)

## Built on by (most cited first, 9 of 9)

- [Robot Behavior-Tree-Based Task Generation with Large Language Models](https://mlsanity.com/paper/2302.12927/) (2023)
- [Behavior Trees in Robot Control Systems](https://mlsanity.com/paper/2203.13083/) (2022)
- [Integrating Intent Understanding and Optimal Behavior Planning for Behavior Tree Generation from Human Instructions](https://mlsanity.com/paper/2405.07474/) (2024)
- [Skill-based Multi-objective Reinforcement Learning of Industrial Robot Tasks with Planning and Knowledge Integration](https://mlsanity.com/paper/2203.10033/) (2022)
- [KT-BT: A Framework for Knowledge Transfer Through Behavior Trees in Multi-Robot Systems](https://mlsanity.com/paper/2209.02886/) (2022)
- [A Study on Training and Developing Large Language Models for Behavior Tree Generation](https://mlsanity.com/paper/2401.08089/) (2024)
- [Designing Robust Cyber-Defense Agents with Evolving Behavior Trees](https://mlsanity.com/paper/2410.16383/) (2024)
- [From Prompts to Pavement: LMMs-based Agentic Behavior-Tree Generation Framework for Autonomous Vehicles](https://mlsanity.com/paper/2601.12358/) (2026)
- [Behavior Tree-Based Task Planning for Multiple Mobile Robots using a Data Distribution Service](https://mlsanity.com/paper/2201.10918/) (2022)

## Abstract (arXiv)

Industrial robots can solve very complex tasks in controlled environments, but modern applications require robots able to operate in unpredictable surroundings as well. An increasingly popular reactive policy architecture in robotics is Behavior Trees but as with other architectures, programming time still drives cost and limits flexibility. There are two main branches of algorithms to generate policies automatically, automated planning and machine learning, both with their own drawbacks. We propose a method for generating Behavior Trees using a Genetic Programming algorithm and combining the two branches by taking the result of an automated planner and inserting it into the population. Experimental results confirm that the proposed method of combining planning and learning performs well on a variety of robotic assembly problems and outperforms both of the base methods used separately. We also show that this type of high level learning of Behavior Trees can be transferred to a real system without further training.