Learning Action Conditions for Automatic Behavior Tree Generation from Human Demonstrations

Abstract

The multitude of possible tasks and user preferences in real-world human-robot interaction scenarios renders pure pre-programming of robotic tasks inadequate. Recently, Behavior Trees (BTs) gained more focus as a modular internal task representation and particularly learning BTs directly from human video demonstrations offers also non-programming experts an opportunity to conveniently teach robots. However, automatically building BTs from human task demonstrations requires task constraints in form of action conditions. While existing work on automated BT generation often relies on pre-defined relevant features and heuristic condition computation, here we propose and evaluate different methods to automatically extract action pre- and post-conditions for BT generation from videos of human demonstrations. In particular, we first reduce the feature space using a correlation-based feature pre-selection, as well as a rule-based pre-selection based on a Decision Tree. Then, we select features that are relevant pre- and post-conditions for particular actions based on three different variance-based methods. We compare the different methods for feature selection and condition extraction on two pick-and-place tasks and discuss advantages and shortcomings of all methods in the context of learning BTs from human demonstrations.