DiffusionHandover: Reliable Human-to-Robot Handover Generation With Anthropomorphic Hand
Yifan Yang, Sizhe Wang, Yongkang Luo, Daheng Li, Yayu Huang, Dashun Yan, Haonan Duan, Peng Wang
Abstract
Human-to-robot handover is a fundamental capability in human-robot interaction, critical for effective collaboration in service and assistive domains. Despite recent progress, ensuring both reliability and safety-particularly collision-free interaction with the human hand-remains a major challenge, especially when using anthropomorphic robotic hands. In this work, we propose DiffusionHandover, a novel framework built on a Decomposed Vector-Quantized VAE (DVQ-VAE) latent diffusion model, further enhanced with reinforcement learning from human feedback (RLHF) to improve grasp reliability and alignment with human preferences. We validate our approach extensively in both simulation and the real world using a Schunk SVH anthropomorphic hand. Our method achieves an average success rate above 80% with diverse grasp configurations on unseen objects. In addition, we conduct ablation studies to assess individual submodules, as well as comparative evaluations against state-of-the-art baselines.
BibTeX
@inproceedings{ral2026_diffusionhandove,
title = {DiffusionHandover: Reliable Human-to-Robot Handover Generation With Anthropomorphic Hand},
author = {Yifan Yang and Sizhe Wang and Yongkang Luo and Daheng Li and Yayu Huang and Dashun Yan and Haonan Duan and Peng Wang},
booktitle = {RA-L 2026},
year = {2026}
}