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arXiv · 2601.19406

Sim-and-Human Co-training for Data-Efficient and Scene-Generalizable Bimanual Manipulation

Kaipeng Fang·Weiqing Liang·Yuyang Li·Ji Zhang·Pengpeng Zeng·Heng Tao Shen·Jingkuan Song·Lianli Gao
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Abstract

Real-robot demonstrations are prohibitively expensive, while simulation data and real-world human demonstrations are both scalable but each leaves a distinct gap: simulation suffers from a sim-to-real visual gap, and human data suffers from a human-to-robot embodiment gap. In this work, we identify a natural yet underexplored complementarity between these sources: simulation contributes robot-valid actions absent in human data, while human data provides real-world observations that simulation struggles to render. Building on this insight, we present SimHum, a co-training recipe that extracts kinematic priors from simulation and visual priors from human observations, then fine-tunes on a small real-robot dataset. SimHum exhibits strong scene-generalizable and data-efficient capabilities. With only 80 real-robot episodes per task, it achieves 62.5% success on held-out OOD scenes across four bimanual tabletop tasks, 53.7% higher than Real only in absolute success rate. Moreover, in a controlled data-collection study with matched collection time, SimHum improves over the best single-source pre-training baseline by 35.0% in absolute success rate. Project page: https://kaipengfang.github.io/sim-and-human/

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Kaipeng Fang, Weiqing Liang, Yuyang Li, Ji Zhang, Pengpeng Zeng, Heng Tao Shen, Jingkuan Song, Lianli Gao. 2026-09-17. Sim-and-Human Co-training for Data-Efficient and Scene-Generalizable Bimanual Manipulation. https://arxiv.org/abs/2601.19406

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