CoBT: Collaborative Programming of Behaviour Trees from One Demonstration for Robot Manipulation
<|reference_start|>CoBT: Collaborative Programming of Behaviour Trees from One Demonstration for Robot Manipulation: 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.<|reference_end|>
@article{jain2024cobt:,
    title={CoBT: Collaborative Programming of Behaviour Trees from One
  Demonstration for Robot Manipulation},
    author={Aayush Jain, Philip Long, Valeria Villani, John D. Kelleher, Maria
  Chiara Leva},
    journal={arXiv preprint arXiv:2404.05870},
    year={2024},
    archivePrefix={arXiv},
    eprint={2404.05870},
    primaryClass={cs.RO}
}