# Object-Independent Human-to-Robot Handovers using Real Time Robotic Vision

arXiv: https://arxiv.org/abs/2006.01797 · HTML: https://mlsanity.com/paper/2006.01797/

## Key facts

- Cited by 17 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 2 Jun 2020.

## Built on by (most cited first, 17 of 17)

- [Learning Human-to-Robot Handovers from Point Clouds](https://mlsanity.com/paper/2303.17592/) (2023)
- [HandoverSim: A Simulation Framework and Benchmark for Human-to-Robot Object Handovers](https://mlsanity.com/paper/2205.09747/) (2022)
- [GenH2R: Learning Generalizable Human-to-Robot Handover via Scalable Simulation, Demonstration, and Imitation](https://mlsanity.com/paper/2401.00929/) (2024)
- [SynH2R: Synthesizing Hand-Object Motions for Learning Human-to-Robot Handovers](https://mlsanity.com/paper/2311.05599/) (2023)
- [Model Predictive Control for Fluid Human-to-Robot Handovers](https://mlsanity.com/paper/2204.00134/) (2022)
- [The CORSMAL benchmark for the prediction of the properties of containers](https://mlsanity.com/paper/2107.12719/) (2021)
- [Sparse multi-view hand-object reconstruction for unseen environments](https://mlsanity.com/paper/2405.01353/) (2024)
- [MoVEInt: Mixture of Variational Experts for Learning Human-Robot Interactions from Demonstrations](https://mlsanity.com/paper/2407.07636/) (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)
- [Affordance segmentation of hand-occluded containers from exocentric images](https://mlsanity.com/paper/2308.11233/) (2023)
- [Stereo Hand-Object Reconstruction for Human-to-Robot Handover](https://mlsanity.com/paper/2412.07487/) (2024)
- [Visual Affordance Prediction: Survey and Reproducibility](https://mlsanity.com/paper/2505.05074/) (2025)
- [AnyOKP: One-Shot and Instance-Aware Object Keypoint Extraction with Pretrained ViT](https://mlsanity.com/paper/2309.08134/) (2023)
- [SPGrasp: Spatiotemporal Prompt-driven Grasp Synthesis in Dynamic Scenes](https://mlsanity.com/paper/2508.20547/) (2025)

## Abstract (arXiv)

We present an approach for safe and object-independent human-to-robot handovers using real time robotic vision and manipulation. We aim for general applicability with a generic object detector, a fast grasp selection algorithm and by using a single gripper-mounted RGB-D camera, hence not relying on external sensors. The robot is controlled via visual servoing towards the object of interest. Putting a high emphasis on safety, we use two perception modules: human body part segmentation and hand/finger segmentation. Pixels that are deemed to belong to the human are filtered out from candidate grasp poses, hence ensuring that the robot safely picks the object without colliding with the human partner. The grasp selection and perception modules run concurrently in real-time, which allows monitoring of the progress. In experiments with 13 objects, the robot was able to successfully take the object from the human in 81.9% of the trials.