A Framework for Learning Behavior Trees in Collaborative Robotic Applications

TL;DR

This paper introduces a framework that combines learning from demonstration and genetic programming to enable non-experts to efficiently generate behavior trees for collaborative robots, validated through manipulation experiments.

Contribution

It presents a novel combined approach for semi-automatic learning of behavior trees, enhancing usability and efficiency in robotic programming.

Findings

Successful transfer of learned behavior trees from simulation to real robots

Enhanced ease of programming for non-expert users

Effective manipulation performance in experiments

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.