Sim-and-Human Co-training for Data-Efficient and Generalizable Robotic Manipulation

• URL: http://arxiv.org/abs/2601.19406v1
• Date: Tue, 27 Jan 2026 09:41:28 GMT
• Title: Sim-and-Human Co-training for Data-Efficient and Generalizable Robotic Manipulation
• Authors: Kaipeng Fang, Weiqing Liang, Yuyang Li, Ji Zhang, Pengpeng Zeng, Lianli Gao, Jingkuan Song, Heng Tao Shen,
• Abstract summary: SimHum is a framework to simultaneously extract kinematic prior from simulated robot actions and visual prior from real-world human observations.<n>Based on the two complementary priors, we achieve data-efficient and generalizable robotic manipulation in real-world tasks.
• Score: 113.13282853889818
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Synthetic simulation data and real-world human data provide scalable alternatives to circumvent the prohibitive costs of robot data collection. However, these sources suffer from the sim-to-real visual gap and the human-to-robot embodiment gap, respectively, which limits the policy's generalization to real-world scenarios. In this work, we identify a natural yet underexplored complementarity between these sources: simulation offers the robot action that human data lacks, while human data provides the real-world observation that simulation struggles to render. Motivated by this insight, we present SimHum, a co-training framework to simultaneously extract kinematic prior from simulated robot actions and visual prior from real-world human observations. Based on the two complementary priors, we achieve data-efficient and generalizable robotic manipulation in real-world tasks. Empirically, SimHum outperforms the baseline by up to $\mathbf{40\%}$ under the same data collection budget, and achieves a $\mathbf{62.5\%}$ OOD success with only 80 real data, outperforming the real only baseline by $7.1\times$. Videos and additional information can be found at \href{https://kaipengfang.github.io/sim-and-human}{project website}.

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