Top-1 CORSMAL Challenge 2020 Submission : Filling Mass Estimation Using Multi-modal Observations of Human-Robot Handovers
Iashin, Vladimir; Palermo, Francesca; Solak, Gökhan; Coppola, Claudio (2021)
Iashin, Vladimir
Palermo, Francesca
Solak, Gökhan
Coppola, Claudio
Teoksen toimittaja(t)
Del Bimbo, Alberto
Cucchiara, Rita
Sclaroff, Stan
Farinella, Giovanni Maria
Mei, Tao
Bertini, Marco
Escalante, Hugo Jair
Vezzani, Roberto
Springer
2021
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Julkaisun pysyvä osoite on
https://urn.fi/URN:NBN:fi:tuni-202210257794
https://urn.fi/URN:NBN:fi:tuni-202210257794
Kuvaus
Peer reviewed
Tiivistelmä
Human-robot object handover is a key skill for the future of human-robot collaboration. CORSMAL 2020 Challenge focuses on the perception part of this problem: the robot needs to estimate the filling mass of a container held by a human. Although there are powerful methods in image processing and audio processing individually, answering such a problem requires processing data from multiple sensors together. The appearance of the container, the sound of the filling, and the depth data provide essential information. We propose a multi-modal method to predict three key indicators of the filling mass: filling type, filling level, and container capacity. These indicators are then combined to estimate the filling mass of a container. Our method obtained Top-1 overall performance among all submissions to CORSMAL 2020 Challenge on both public and private subsets while showing no evidence of overfitting. Our source code is publicly available: github.com/v-iashin/CORSMAL.
Kokoelmat
- TUNICRIS-julkaisut [16726]