# Object Handovers: a Review for Robotics

arXiv: https://arxiv.org/abs/2007.12952 · HTML: https://mlsanity.com/paper/2007.12952/

## Key facts

- Cited by 28 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 25 Jul 2020.

## Built on by (most cited first, 28 of 28)

- [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)
- [HOI4ABOT: Human-Object Interaction Anticipation for Human Intention Reading Collaborative roBOTs](https://mlsanity.com/paper/2309.16524/) (2023)
- [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)
- [Safety Control of Service Robots with LLMs and Embodied Knowledge Graphs](https://mlsanity.com/paper/2405.17846/) (2024)
- [The CORSMAL benchmark for the prediction of the properties of containers](https://mlsanity.com/paper/2107.12719/) (2021)
- [ROSGPT_Vision: Commanding Robots Using Only Language Models' Prompts](https://mlsanity.com/paper/2308.11236/) (2023)
- [A mixed-reality dataset for category-level 6D pose and size estimation of hand-occluded containers](https://mlsanity.com/paper/2211.10470/) (2022)
- [ContactHandover: Contact-Guided Robot-to-Human Object Handover](https://mlsanity.com/paper/2404.01402/) (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)
- [Learning to Detect Slip with Barometric Tactile Sensors and a Temporal Convolutional Neural Network](https://mlsanity.com/paper/2202.09549/) (2022)
- [FuRPE: Learning Full-body Reconstruction from Part Experts](https://mlsanity.com/paper/2212.00731/) (2022)
- [GoferBot: A Visual Guided Human-Robot Collaborative Assembly System](https://mlsanity.com/paper/2304.08840/) (2023)
- [Under Pressure: Learning to Detect Slip with Barometric Tactile Sensors](https://mlsanity.com/paper/2103.13460/) (2021)
- [Robot to Human Object Handover using Vision and Joint Torque Sensor Modalities](https://mlsanity.com/paper/2210.15085/) (2022)
- [Kinematically Constrained Human-like Bimanual Robot-to-Human Handovers](https://mlsanity.com/paper/2402.14525/) (2024)
- [Intent-Handover: Grounding Language in Human-Usage Regions for Trustworthy Robot-to-Human Handovers](https://mlsanity.com/paper/2503.03579/) (2025)
- [Learning Dexterous Object Handover](https://mlsanity.com/paper/2506.16822/) (2025)
- [A Distributed Multi-Modal Sensing Approach for Human Activity Recognition in Real-Time Human-Robot Collaboration](https://mlsanity.com/paper/2602.07024/) (2026)
- [Improving through Interaction: Searching Behavioral Representation Spaces with CMA-ES-IG](https://mlsanity.com/paper/2603.09011/) (2026)
- [A Multimodal RGB and Events Dataset for Hand Detection in First-Person View](https://mlsanity.com/paper/2606.10790/) (2026)
- [RGB-D Video Generation for Improving Human-to-Robot Object Handover Prediction](https://mlsanity.com/paper/2608.13028/) (2026)

## Abstract (arXiv)

This article surveys the literature on human-robot object handovers. A handover is a collaborative joint action where an agent, the giver, gives an object to another agent, the receiver. The physical exchange starts when the receiver first contacts the object held by the giver and ends when the giver fully releases the object to the receiver. However, important cognitive and physical processes begin before the physical exchange, including initiating implicit agreement with respect to the location and timing of the exchange. From this perspective, we structure our review into the two main phases delimited by the aforementioned events: 1) a pre-handover phase, and 2) the physical exchange. We focus our analysis on the two actors (giver and receiver) and report the state of the art of robotic givers (robot-to-human handovers) and the robotic receivers (human-to-robot handovers). We report a comprehensive list of qualitative and quantitative metrics commonly used to assess the interaction. While focusing our review on the cognitive level (e.g., prediction, perception, motion planning, learning) and the physical level (e.g., motion, grasping, grip release) of the handover, we briefly discuss also the concepts of safety, social context, and ergonomics. We compare the behaviours displayed during human-to-human handovers to the state of the art of robotic assistants, and identify the major areas of improvement for robotic assistants to reach performance comparable to human interactions. Finally, we propose a minimal set of metrics that should be used in order to enable a fair comparison among the approaches.