Learning Human-like Hand Reaching for Human-Robot Handshaking

TL;DR

This paper introduces a new framework enabling humanoid robots to learn human-like hand-reaching for handshaking using third-person human interaction data, facilitating adaptable and robot-agnostic social behaviors.

Contribution

A novel learning framework for human-like hand-reaching in robots using third-person data, avoiding the need for robot-specific re-training or kinesthetic demonstrations.

Findings

Successfully applied on two different humanoid robots.

Achieved natural and human-like reaching behaviors.

Framework is adaptable to various robot shapes and control limits.

Abstract

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.