arXiv subjects

Hongbiao Dong

Publications and source records attributed to Hongbiao Dong.

3 recordsLinked to original sources

arXiv2026

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

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/.

cs.RO↗

arXiv2026

Embodied Interpretability: Linking Causal Understanding to Generalization in Vision-Language-Action Models

Vision-Language-Action (VLA) policies often fail under distribution shift, suggesting that decisions may depend on spurious visual correlations rather than task-relevant causes. We formulate visual-action attribution as an interventional estimation problem. Accordingly, we introduce the Interventional Significance Score (ISS), an interventional masking procedure for estimating the causal influence of visual regions on action predictions, and the Nuisance Mass Ratio (NMR), a scalar measure of attribution to task-irrelevant features. We analyze the statistical properties of ISS and show that it admits unbiased estimation, and we characterize conditions under which action prediction error provides a valid proxy for causal influence. Experiments across diverse manipulation tasks indicate that NMR predicts generalization behavior and that ISS yields more faithful explanations than existing interpretability methods. These results suggest that interventional attribution provides a simple diagnostic approach for identifying causal misalignment in embodied policies.

cs.RO↗

arXiv2025

Intent-Handover: Grounding Language in Human-Usage Regions for Trustworthy Robot-to-Human Handovers

Spoken instructions in robot-to-human handovers may specify either an object ("the cup") or an intended use ("pour water"); in both cases, successful handover requires the robot to infer the target object and the region remaining available for the human to hold. If the robot grasps that hold region, the object could become awkward to receive and immediately use, potentially reducing perceived competence and trust; if the gripper approaches too close to the receiving hand during delivery, perceived safety may also suffer. We present Intent-Handover, which grounds unconstrained speech and visual scene context into explicit grasp and delivery constraints. Given a spoken instruction and a scene observation, a vision-language model identifies the target object and the intended human-usage region. A grasp optimization module then selects a feasible grasp keeping this region accessible while enforcing clearance from the predicted receiving hand. During execution, the robot tracks upper-body key points to estimate the user's receiving pose and places the handover at an ergonomically feasible location. In a within-subjects ablation study (n=30), human-usage region awareness increases perceived trust, hand-gripper collision avoidance increases perceived safety, and interaction comfort is highest when both are enabled. Website and code: https://robot-future.github.io/intent-handover/.

cs.RO↗