# CoBT: Collaborative Programming of Behaviour Trees from One Demonstration for Robot Manipulation

arXiv: https://arxiv.org/abs/2404.05870 · HTML: https://mlsanity.com/paper/2404.05870/

## Key facts

- Cited by 2 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 8 Apr 2024.

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

- [Adaptive Manipulation using Behavior Trees](https://mlsanity.com/paper/2406.14634/) (2024)
- [See and Switch: Vision-Based Branching for Interactive Robot-Skill Programming](https://mlsanity.com/paper/2603.08057/) (2026)

## Abstract (arXiv)

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.