  Vision-based human-to-robot handover is an important and challenging task in
human-robot interaction. Recent work has attempted to train robot policies by
interacting with dynamic virtual humans in simulated environments, where the
policies can later be transferred to the real world. However, a major
bottleneck is the reliance on human motion capture data, which is expensive to
acquire and difficult to scale to arbitrary objects and human grasping motions.
In this paper, we introduce a framework that can generate plausible human
grasping motions suitable for training the robot. To achieve this, we propose a
hand-object synthesis method that is designed to generate handover-friendly
motions similar to humans. This allows us to generate synthetic training and
testing data with 100x more objects than previous work. In our experiments, we
show that our method trained purely with synthetic data is competitive with
state-of-the-art methods that rely on real human motion data both in simulation
and on a real system. In addition, we can perform evaluations on a larger scale
compared to prior work. With our newly introduced test set, we show that our
model can better scale to a large variety of unseen objects and human motions
compared to the baselines. Project page:
https://eth-ait.github.io/synthetic-handovers/
