# Learning Human-like Hand Reaching for Human-Robot Handshaking

arXiv: https://arxiv.org/abs/2103.00616 · HTML: https://mlsanity.com/paper/2103.00616/

## Key facts

- Cited by 4 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 28 Feb 2021.

## Built on by (most cited first, 4 of 4)

- [MILD: Multimodal Interactive Latent Dynamics for Learning Human-Robot Interaction](https://mlsanity.com/paper/2210.12418/) (2022)
- [MoVEInt: Mixture of Variational Experts for Learning Human-Robot Interactions from Demonstrations](https://mlsanity.com/paper/2407.07636/) (2024)
- [Learning Multimodal Latent Dynamics for Human-Robot Interaction](https://mlsanity.com/paper/2311.16380/) (2023)
- [Behavior Foundations for Quadruped Robots: ABot-C0 Technical Report](https://mlsanity.com/paper/2607.07370/) (2026)

## Abstract (arXiv)

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.