# Reactive Human-to-Robot Handovers of Arbitrary Objects

arXiv: https://arxiv.org/abs/2011.08961 · HTML: https://mlsanity.com/paper/2011.08961/

## Key facts

- Cited by 25 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 17 Nov 2020.

## Built on by (most cited first, 25 of 25)

- [A Survey of Embodied Learning for Object-Centric Robotic Manipulation](https://mlsanity.com/paper/2408.11537/) (2024)
- [Learning Human-to-Robot Handovers from Point Clouds](https://mlsanity.com/paper/2303.17592/) (2023)
- [Co-GAIL: Learning Diverse Strategies for Human-Robot Collaboration](https://mlsanity.com/paper/2108.06038/) (2021)
- [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)
- [Learning Perceptual Concepts by Bootstrapping from Human Queries](https://mlsanity.com/paper/2111.05251/) (2021)
- [DA2 Dataset: Toward Dexterity-Aware Dual-Arm Grasping](https://mlsanity.com/paper/2208.00408/) (2022)
- [Keypoint-GraspNet: Keypoint-based 6-DoF Grasp Generation from the Monocular RGB-D input](https://mlsanity.com/paper/2209.08752/) (2022)
- [Model Predictive Control for Fluid Human-to-Robot Handovers](https://mlsanity.com/paper/2204.00134/) (2022)
- [Real-time Holistic Robot Pose Estimation with Unknown States](https://mlsanity.com/paper/2402.05655/) (2024)
- [The CORSMAL benchmark for the prediction of the properties of containers](https://mlsanity.com/paper/2107.12719/) (2021)
- [Enhancing Generalizable 6D Pose Tracking of an In-Hand Object with Tactile Sensing](https://mlsanity.com/paper/2210.04026/) (2022)
- [Sparse multi-view hand-object reconstruction for unseen environments](https://mlsanity.com/paper/2405.01353/) (2024)
- [RHINO: Learning Real-Time Humanoid-Human-Object Interaction from Human Demonstrations](https://mlsanity.com/paper/2502.13134/) (2025)
- [Task-Oriented 6-DoF Grasp Pose Detection in Clutters](https://mlsanity.com/paper/2502.16976/) (2025)
- [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)
- [Depth Restoration of Hand-Held Transparent Objects for Human-to-Robot Handover](https://mlsanity.com/paper/2408.14997/) (2024)
- [PoseDiff: A Unified Diffusion Model Bridging Robot Pose Estimation and Video-to-Action Control](https://mlsanity.com/paper/2509.24591/) (2025)
- [RGB-D Video Generation for Improving Human-to-Robot Object Handover Prediction](https://mlsanity.com/paper/2608.13028/) (2026)

## Abstract (arXiv)

Human-robot object handovers have been an actively studied area of robotics over the past decade; however, very few techniques and systems have addressed the challenge of handing over diverse objects with arbitrary appearance, size, shape, and rigidity. In this paper, we present a vision-based system that enables reactive human-to-robot handovers of unknown objects. Our approach combines closed-loop motion planning with real-time, temporally-consistent grasp generation to ensure reactivity and motion smoothness. Our system is robust to different object positions and orientations, and can grasp both rigid and non-rigid objects. We demonstrate the generalizability, usability, and robustness of our approach on a novel benchmark set of 26 diverse household objects, a user study with naive users (N=6) handing over a subset of 15 objects, and a systematic evaluation examining different ways of handing objects. More results and videos can be found at https://sites.google.com/nvidia.com/handovers-of-arbitrary-objects.