  Human-robot collaboration requires the contactless estimation of the physical
properties of containers manipulated by a person, for example while pouring
content in a cup or moving a food box. Acoustic and visual signals can be used
to estimate the physical properties of such objects, which may vary
substantially in shape, material and size, and also be occluded by the hands of
the person. To facilitate comparisons and stimulate progress in solving this
problem, we present the CORSMAL challenge and a dataset to assess the
performance of the algorithms through a set of well-defined performance scores.
The tasks of the challenge are the estimation of the mass, capacity, and
dimensions of the object (container), and the classification of the type and
amount of its content. A novel feature of the challenge is our
real-to-simulation framework for visualising and assessing the impact of
estimation errors in human-to-robot handovers.
