  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: https://github.com/v-iashin/CORSMAL
