# Towards safe human-to-robot handovers of unknown containers

arXiv: https://arxiv.org/abs/2107.01309 · HTML: https://mlsanity.com/paper/2107.01309/

## Key facts

- Cited by 11 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 3 Jul 2021.

## Built on by (most cited first, 11 of 11)

- [Learning Human-to-Robot Handovers from Point Clouds](https://mlsanity.com/paper/2303.17592/) (2023)
- [Safe Reinforcement Learning of Dynamic High-Dimensional Robotic Tasks: Navigation, Manipulation, Interaction](https://mlsanity.com/paper/2209.13308/) (2022)
- [Safe Reinforcement Learning on the Constraint Manifold: Theory and Applications](https://mlsanity.com/paper/2404.09080/) (2024)
- [SynH2R: Synthesizing Hand-Object Motions for Learning Human-to-Robot Handovers](https://mlsanity.com/paper/2311.05599/) (2023)
- [Sparse multi-view hand-object reconstruction for unseen environments](https://mlsanity.com/paper/2405.01353/) (2024)
- [Segmenting Object Affordances: Reproducibility and Sensitivity to Scale](https://mlsanity.com/paper/2409.01814/) (2024)
- [Improving Generalization of Deep Networks for Estimating Physical Properties of Containers and Fillings](https://mlsanity.com/paper/2203.01192/) (2022)
- [Container Localisation and Mass Estimation with an RGB-D Camera](https://mlsanity.com/paper/2203.01207/) (2022)
- [Audio-Visual Object Classification for Human-Robot Collaboration](https://mlsanity.com/paper/2203.01977/) (2022)
- [Stereo Hand-Object Reconstruction for Human-to-Robot Handover](https://mlsanity.com/paper/2412.07487/) (2024)
- [Reproducible Multimodal Affordance Prediction](https://mlsanity.com/paper/2608.18317/) (2026)

## Abstract (arXiv)

Safe human-to-robot handovers of unknown objects require accurate estimation of hand poses and object properties, such as shape, trajectory, and weight. Accurately estimating these properties requires the use of scanned 3D object models or expensive equipment, such as motion capture systems and markers, or both. However, testing handover algorithms with robots may be dangerous for the human and, when the object is an open container with liquids, for the robot. In this paper, we propose a real-to-simulation framework to develop safe human-to-robot handovers with estimations of the physical properties of unknown cups or drinking glasses and estimations of the human hands from videos of a human manipulating the container. We complete the handover in simulation, and we estimate a region that is not occluded by the hand of the human holding the container. We also quantify the safeness of the human and object in simulation. We validate the framework using public recordings of containers manipulated before a handover and show the safeness of the handover when using noisy estimates from a range of perceptual algorithms.