Abstract
While industrial collaborative robot programming becomes more and more accessible, building adaptable and modular behaviors is still out of reach for most non-programmer experts. Allowing any human to teach cobots complex and personalized behaviors with natural means of communication would likely ease their acceptability and long-term co-integration in industries of all sizes. In this paper, we present a prototype of robotic cognitive architecture for interactive task learning (ITL) integrating human preferences in a human-robot collaborative industrial context. The architecture is based on integrating connectionist modules, such as deep learning modules, and symbolic semantic graphs, such as behavior trees, for modular skill representations and learning. An experimental validation was made on a real UR10e industrial collaborative robot. The cobot is taught, online and incrementally, a simple task with variations based on human preferences.
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Hélénon, F., Thiery, S., Nyiri, E., Gibaru, O. (2024). A Cognitive Architecture for Human-Aware Interactive Robot Learning with Industrial Collaborative Robots. In: Marques, L., Santos, C., Lima, J.L., Tardioli, D., Ferre, M. (eds) Robot 2023: Sixth Iberian Robotics Conference. ROBOT 2023. Lecture Notes in Networks and Systems, vol 976. Springer, Cham. https://doi.org/10.1007/978-3-031-58676-7_34
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