# 3HANDS Dataset: Learning from Humans for Generating Naturalistic Handovers with Supernumerary Robotic Limbs

arXiv: https://arxiv.org/abs/2503.04635 · HTML: https://mlsanity.com/paper/2503.04635/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 13 of its 88 references that are in the map.
- First posted on arXiv 6 Mar 2025.

## Builds on (most cited first, 13 of 13)

- [On the Continuity of Rotation Representations in Neural Networks](https://mlsanity.com/paper/1812.07035/) (2018)
- [HuMoR: 3D Human Motion Model for Robust Pose Estimation](https://mlsanity.com/paper/2105.04668/) (2021)
- [Character Controllers Using Motion VAEs](https://mlsanity.com/paper/2103.14274/) (2021)
- [IMos: Intent-Driven Full-Body Motion Synthesis for Human-Object Interactions](https://mlsanity.com/paper/2212.07555/) (2022)
- [WANDR: Intention-guided Human Motion Generation](https://mlsanity.com/paper/2404.15383/) (2024)
- [H2O: A Benchmark for Visual Human-human Object Handover Analysis](https://mlsanity.com/paper/2104.11466/) (2021)
- [Model Predictive Control for Fluid Human-to-Robot Handovers](https://mlsanity.com/paper/2204.00134/) (2022)
- [HOH: Markerless Multimodal Human-Object-Human Handover Dataset with Large Object Count](https://mlsanity.com/paper/2310.00723/) (2023)
- [MILD: Multimodal Interactive Latent Dynamics for Learning Human-Robot Interaction](https://mlsanity.com/paper/2210.12418/) (2022)
- [An Experimental Validation and Comparison of Reaching Motion Models for Unconstrained Handovers: Towards Generating Humanlike Motions for Human-Robot Handovers](https://mlsanity.com/paper/2108.12780/) (2021)
- [Sensitivity analysis using the Metamodel of Optimal Prognosis](https://mlsanity.com/paper/2408.03590/) (2024)
- [Givers & Receivers perceive handover tasks differently: Implications for Human-Robot collaborative system design](https://mlsanity.com/paper/1708.06207/) (2017)
- [Mapping Human Muscle Force to Supernumerary Robotics Device for Overhead Task Assistance](https://mlsanity.com/paper/2107.13799/) (2021)

## Abstract (arXiv)

Supernumerary robotic limbs (SRLs) are robotic structures integrated closely with the user's body, which augment human physical capabilities and necessitate seamless, naturalistic human-machine interaction. For effective assistance in physical tasks, enabling SRLs to hand over objects to humans is crucial. Yet, designing heuristic-based policies for robots is time-consuming, difficult to generalize across tasks, and results in less human-like motion. When trained with proper datasets, generative models are powerful alternatives for creating naturalistic handover motions. We introduce 3HANDS, a novel dataset of object handover interactions between a participant performing a daily activity and another participant enacting a hip-mounted SRL in a naturalistic manner. 3HANDS captures the unique characteristics of SRL interactions: operating in intimate personal space with asymmetric object origins, implicit motion synchronization, and the user's engagement in a primary task during the handover. To demonstrate the effectiveness of our dataset, we present three models: one that generates naturalistic handover trajectories, another that determines the appropriate handover endpoints, and a third that predicts the moment to initiate a handover. In a user study (N=10), we compare the handover interaction performed with our method compared to a baseline. The findings show that our method was perceived as significantly more natural, less physically demanding, and more comfortable.