Abstract
The paper discusses an approach for teleoperating a mobile robot based on qualitative spatial relations, which are instructed through speech-based and deictic commands. Given a workspace containing a robot, a user and some objects, we exploit fuzzy reasoning criteria to describe the pertinence map between the locations in the workspace and qualitative commands incrementally acquired. We discuss the modularity features of the used reasoning technique through some use cases addressing a conjunction of spatial kernels. In particular, we address the problem of finding a suitable target location from a set of qualitative spatial relations based on symbolic reasoning and Monte Carlo simulations. Our architecture is analyzed in a scenario considering simple kernels and an almost-perfect perception of the environment. Nevertheless, the presented approach is modular and scalable, and it could be also exploited to design application where multi-modal qualitative interactions are considered.
L. Buoncompagni, S. Ghosh and M. Moura—These authors contributed equally to this work.
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available at https://github.com/EmaroLab/mmodal_teleop.
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Buoncompagni, L., Ghosh, S., Moura, M., Mastrogiovanni, F. (2018). A Scalable Architecture to Design Multi-modal Interactions for Qualitative Robot Navigation. In: Ghidini, C., Magnini, B., Passerini, A., Traverso, P. (eds) AI*IA 2018 – Advances in Artificial Intelligence. AI*IA 2018. Lecture Notes in Computer Science(), vol 11298. Springer, Cham. https://doi.org/10.1007/978-3-030-03840-3_8
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