Learning Human-like Hand Reaching for Human-Robot Handshaking
This addresses the challenge of teaching non-backdrivable robots social interaction skills, which is incremental as it adapts existing learning methods to a specific domain.
The paper tackles the problem of enabling humanoid robots to perform human-like handshakes by learning reaching behaviors from third-person human-human interaction data, achieving successful execution on two different humanoid robots without retraining.
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.