  Safe human-to-robot handovers of unknown objects require accurate estimation
of hand poses and object properties, such as shape, trajectory, and weight.
Accurately estimating these properties requires the use of scanned 3D object
models or expensive equipment, such as motion capture systems and markers, or
both. However, testing handover algorithms with robots may be dangerous for the
human and, when the object is an open container with liquids, for the robot. In
this paper, we propose a real-to-simulation framework to develop safe
human-to-robot handovers with estimations of the physical properties of unknown
cups or drinking glasses and estimations of the human hands from videos of a
human manipulating the container. We complete the handover in simulation, and
we estimate a region that is not occluded by the hand of the human holding the
container. We also quantify the safeness of the human and object in simulation.
We validate the framework using public recordings of containers manipulated
before a handover and show the safeness of the handover when using noisy
estimates from a range of perceptual algorithms.
