Mass customization and shorter manufacturing cycles are becoming more important among small and medium-sized companies. However, classical industrial robots struggle to cope with product variation and dynamic environments. In this paper, we present CoBT, a collaborative programming by demonstration framework for generating reactive and modular behavior trees. CoBT relies on a single demonstration and a combination of data-driven machine learning methods with logic-based declarative learning to learn a task, thus eliminating the need for programming expertise or long development times. The proposed framework is experimentally validated on 7 manipulation tasks and we show that CoBT achieves approx. 93% success rate overall with an average of 7.5s programming time. We conduct a pilot study with non-expert users to provide feedback regarding the usability of CoBT.
@article{arxiv.2404.05870,
title = {CoBT: Collaborative Programming of Behaviour Trees from One Demonstration for Robot Manipulation},
author = {Aayush Jain and Philip Long and Valeria Villani and John D. Kelleher and Maria Chiara Leva},
journal= {arXiv preprint arXiv:2404.05870},
year = {2024}
}