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| Main Authors: | , , , , |
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| Format: | Preprint |
| Published: |
2024
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| Subjects: | |
| Online Access: | https://arxiv.org/abs/2404.05870 |
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| _version_ | 1866911835471478784 |
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| author | Jain, Aayush Long, Philip Villani, Valeria Kelleher, John D. Leva, Maria Chiara |
| author_facet | Jain, Aayush Long, Philip Villani, Valeria Kelleher, John D. Leva, Maria Chiara |
| contents | 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. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2404_05870 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | CoBT: Collaborative Programming of Behaviour Trees from One Demonstration for Robot Manipulation Jain, Aayush Long, Philip Villani, Valeria Kelleher, John D. Leva, Maria Chiara Robotics 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. |
| title | CoBT: Collaborative Programming of Behaviour Trees from One Demonstration for Robot Manipulation |
| topic | Robotics |
| url | https://arxiv.org/abs/2404.05870 |