# Human Grasp Classification for Reactive Human-to-Robot Handovers

arXiv: https://arxiv.org/abs/2003.06000 · HTML: https://mlsanity.com/paper/2003.06000/

## Key facts

- Cited by 13 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 12 Mar 2020.

## Built on by (most cited first, 13 of 13)

- [DexYCB: A Benchmark for Capturing Hand Grasping of Objects](https://mlsanity.com/paper/2104.04631/) (2021)
- [OakInk: A Large-scale Knowledge Repository for Understanding Hand-Object Interaction](https://mlsanity.com/paper/2203.15709/) (2022)
- [P2P: Tuning Pre-trained Image Models for Point Cloud Analysis with Point-to-Pixel Prompting](https://mlsanity.com/paper/2208.02812/) (2022)
- [Tracking and Reconstructing Hand Object Interactions from Point Cloud Sequences in the Wild](https://mlsanity.com/paper/2209.12009/) (2022)
- [H2O: A Benchmark for Visual Human-human Object Handover Analysis](https://mlsanity.com/paper/2104.11466/) (2021)
- [HandoverSim: A Simulation Framework and Benchmark for Human-to-Robot Object Handovers](https://mlsanity.com/paper/2205.09747/) (2022)
- [CP3: Channel Pruning Plug-in for Point-based Networks](https://mlsanity.com/paper/2303.13097/) (2023)
- [Model Predictive Control for Fluid Human-to-Robot Handovers](https://mlsanity.com/paper/2204.00134/) (2022)
- [Anthropomorphic Grasping with Neural Object Shape Completion](https://mlsanity.com/paper/2311.02510/) (2023)
- [Preemptive Motion Planning for Human-to-Robot Indirect Placement Handovers](https://mlsanity.com/paper/2203.00156/) (2022)
- [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)
- [Robot to Human Object Handover using Vision and Joint Torque Sensor Modalities](https://mlsanity.com/paper/2210.15085/) (2022)

## Abstract (arXiv)

Transfer of objects between humans and robots is a critical capability for collaborative robots. Although there has been a recent surge of interest in human-robot handovers, most prior research focus on robot-to-human handovers. Further, work on the equally critical human-to-robot handovers often assumes humans can place the object in the robot's gripper. In this paper, we propose an approach for human-to-robot handovers in which the robot meets the human halfway, by classifying the human's grasp of the object and quickly planning a trajectory accordingly to take the object from the human's hand according to their intent. To do this, we collect a human grasp dataset which covers typical ways of holding objects with various hand shapes and poses, and learn a deep model on this dataset to classify the hand grasps into one of these categories. We present a planning and execution approach that takes the object from the human hand according to the detected grasp and hand position, and replans as necessary when the handover is interrupted. Through a systematic evaluation, we demonstrate that our system results in more fluent handovers versus two baselines. We also present findings from a user study (N = 9) demonstrating the effectiveness and usability of our approach with naive users in different scenarios. More results and videos can be found at http://wyang.me/handovers.