  One of the first and foremost non-verbal interactions that humans perform is
a handshake. It has an impact on first impressions as touch can convey complex
emotions. This makes handshaking an important skill for the repertoire of a
social robot. In this paper, we present a novel framework for learning reaching
behaviours for human-robot handshaking behaviours for humanoid robots solely
using third-person human-human interaction data. This is especially useful for
non-backdrivable robots that cannot be taught by demonstrations via kinesthetic
teaching. Our approach can be easily executed on different humanoid robots.
This removes the need for re-training, which is especially tedious when
training with human-interaction partners. We show this by applying the learnt
behaviours on two different humanoid robots with similar degrees of freedom but
different shapes and control limits.
