Abstract
Object handover is a fundamental and essential capability for robots interacting with humans in many applications such as household chores. In this challenge, we estimate the physical properties of a variety of containers with different fillings such as container capacity and the type and percentage of the content to achieve collaborative physical handover between humans and robots. We introduce multi-modal prediction models using audio-visual-datasets of people interacting with containers distributed by CORSMAL.
R. Ishikawa and Y. Nagao—Equal contribution.
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Ishikawa, R., Nagao, Y., Hachiuma, R., Saito, H. (2021). Audio-Visual Hybrid Approach for Filling Mass Estimation. In: Del Bimbo, A., et al. Pattern Recognition. ICPR International Workshops and Challenges. ICPR 2021. Lecture Notes in Computer Science(), vol 12668. Springer, Cham. https://doi.org/10.1007/978-3-030-68793-9_32
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DOI: https://doi.org/10.1007/978-3-030-68793-9_32
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