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New dataset and generative pipeline enhance human-to-robot handover prediction

Researchers have developed Hand2Bot, a new dataset and generative pipeline called PassGen, aimed at improving human-to-robot object handover prediction. PassGen utilizes Stable Video Diffusion and an Intention-Aware Temporal Face Encoder to create realistic handover sequences, addressing the scarcity of data and the sim-to-real gap in human-robot collaboration. The system incorporates a morphology-based depth editing strategy to mimic real-world sensor noise, enabling more robust zero-shot transfer and earlier intention anticipation for socially aware robotic behavior. AI

IMPACT Enhances human-robot collaboration by improving intention anticipation and enabling socially aware robotic behavior in shared workspaces.

RANK_REASON The cluster describes a new dataset and generative pipeline for a specific research problem in robotics, published on arXiv. [lever_c_demoted from research: ic=1 ai=1.0]

Read on arXiv cs.CV →

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New dataset and generative pipeline enhance human-to-robot handover prediction

COVERAGE [1]

  1. arXiv cs.CV TIER_1 English(EN) · Tianyu Sun, Zhoujie Fu, Zihui Gao, Bang Zhang, Guosheng Lin ·

    RGB-D Video Generation for Improving Human-to-Robot Object Handover Prediction

    arXiv:2608.13028v1 Announce Type: new Abstract: Human-to-robot (H2R) object handover is a fundamental capability for human-robot collaboration, yet progress is hindered by the scarcity of large-scale, human-centric datasets and the significant sim-to-real gap. To address these ch…