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arXiv · 2404.05870

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

Aayush Jain·Philip Long·Valeria Villani·John D. Kelleher·Maria Chiara Leva
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Abstract

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.

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Aayush Jain, Philip Long, Valeria Villani, John D. Kelleher, Maria Chiara Leva. 2024-04-08. CoBT: Collaborative Programming of Behaviour Trees from One Demonstration for Robot Manipulation. https://arxiv.org/abs/2404.05870

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