Learning Submodular Objectives for Team Environmental Monitoring
نویسندگان
چکیده
In this paper, we study the well-known team orienteering problem where a fleet of robots collects rewards by visiting locations. Usually, are assumed to be known robots; however, in applications such as environmental monitoring or scene reconstruction, often subjective and specifying them is challenging. We propose framework learn unknown preferences user presenting alternative solutions them, provides ranking on proposed solutions. consider two cases for user: 1) deterministic which optimal solutions, 2) noisy according an probability distribution. For minimize bound maximum deviation from solution, namely regret. adapt approach capture expected Finally, demonstrate importance learning performance methods extensive set experimental results using real world datasets problems.
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ژورنال
عنوان ژورنال: IEEE robotics and automation letters
سال: 2022
ISSN: ['2377-3766']
DOI: https://doi.org/10.1109/lra.2021.3135928