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CoBT: Collaborative Programming of Behaviour Trees from One Demonstration for Robot Manipulation (2404.05870v2)

Published 8 Apr 2024 in cs.RO

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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Authors (5)
  1. Aayush Jain (10 papers)
  2. Philip Long (8 papers)
  3. Valeria Villani (12 papers)
  4. John D. Kelleher (37 papers)
  5. Maria Chiara Leva (3 papers)
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