# A Multimodal Data Set of Human Handovers with Design Implications for Human-Robot Handovers

arXiv: https://arxiv.org/abs/2304.02154 · HTML: https://mlsanity.com/paper/2304.02154/

## Key facts

- Cited by 4 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 4 Apr 2023.

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

- [CORE4D: A 4D Human-Object-Human Interaction Dataset for Collaborative Object REarrangement](https://mlsanity.com/paper/2406.19353/) (2024)
- [HOH: Markerless Multimodal Human-Object-Human Handover Dataset with Large Object Count](https://mlsanity.com/paper/2310.00723/) (2023)
- [A Comparative Study of Human Activity Recognition: Motion, Tactile, and multi-modal Approaches](https://mlsanity.com/paper/2505.08657/) (2025)
- [A Distributed Multi-Modal Sensing Approach for Human Activity Recognition in Real-Time Human-Robot Collaboration](https://mlsanity.com/paper/2602.07024/) (2026)

## Abstract (arXiv)

Handovers are basic yet sophisticated motor tasks performed seamlessly by humans. They are among the most common activities in our daily lives and social environments. This makes mastering the art of handovers critical for a social and collaborative robot. In this work, we present an experimental study that involved human-human handovers by 13 pairs, i.e., 26 participants. We record and explore multiple features of handovers amongst humans aimed at inspiring handovers amongst humans and robots. With this work, we further create and publish a novel data set of 8672 handovers, bringing together human motion and the forces involved. We further analyze the effect of object weight and the role of visual sensory input in human-human handovers, as well as possible design implications for robots. As a proof of concept, the data set was used for creating a human-inspired data-driven strategy for robotic grip release in handovers, which was demonstrated to result in better robot to human handovers.