R2HandoverSim: A Simulation Framework and Benchmark for Robot-to-Human Object Handovers

Hanxin Zhang; Abdulqader Dhafer; Hongbiao Dong; Zhou Daniel Hao

cs.ROJun 19, 2026

R2HandoverSim: A Simulation Framework and Benchmark for Robot-to-Human Object Handovers

Authors: Hanxin Zhang, Abdulqader Dhafer, Hongbiao Dong, Zhou Daniel Hao

Abstract

We present R2HandoverSim, a simulation benchmark for robot-to-human (R2H) object handovers. Although R2H handover methods have advanced rapidly, the lack of standardized evaluation protocols impedes objective comparison. Our benchmark enables reproducible evaluation by systematically comparing four baselines on their predicted shared grasp poses. We conduct a user study with 30 participants, analyze baseline performance, and show that simulation results correlate with real-world evaluation outcomes. Crucially, five complementary metrics (planning feasibility, reachability, grasp stability, grasp affordance, and safety) better reflect user-perceived handover quality than overall success rate alone. Website and code: https://robot-future.github.io/r2handoversim/.

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Sep 21, 2026 · cs.ROJ/K move · Enter open · S save

H2RBench: A Real-to-Sim Benchmark for Evaluating Human-to-Robot Transfer

Chuyang Xiao, Haotian Zhan, Sriram Krishna, Peilin Meng+3

Learning robot manipulation policies from human video demonstrations constitutes a promising avenue for scalable robot learning. However, comparing different human-to-robot (H2R) transfer methods remains challenging, as existing approaches are evaluated under different settings, including differing task suites, scene layouts, object instances, and amounts of robot supervision. To address this challenge, we present H2RBench, a Real2Sim benchmark for evaluating H2R transfer methods. H2RBench provides a standardized protocol built on real human video demonstrations and simulated robot demonstrations, and includes four manipulation tasks spanning diverse interaction requirements. We evaluate multiple representative H2R transfer methods, each adopting a different strategy for bridging the embodiment gap. Using H2RBench, we systematically characterize how each method scales with the amount of human demonstrations, revealing that methods differ substantially in their ability to leverage additional human data. We further show that simulation performance is broadly predictive of real-world robot performance, with an overall Pearson correlation of r = 0.89, Spearman correlation of \r{ho} = 0.85 and Mean Maximum Rank Violation (MMRV) of 0.06 across method-task configurations. These results establish H2RBench as a practical and scalable benchmark for comparative H2R evaluation prior to real-world deployment.

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Abstract

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