[2605.05241] DexSim2Real: Foundation Model-Guided Sim-to-Real Transfer for Generalizable Dexterous Manipulation
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Computer Science > Robotics
arXiv:2605.05241 (cs)
[Submitted on 3 May 2026]
Title:DexSim2Real: Foundation Model-Guided Sim-to-Real Transfer for Generalizable Dexterous Manipulation
Authors:Zijian Zeng, Fei Ding, Huiming Yang, Xianwei Li, Yuhao Liao
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Abstract:Sim-to-real transfer remains a critical bottleneck for deploying dexterous manipulation policies learned in simulation to real-world robots. Existing approaches rely on manually designed domain randomization or task-specific adaptation, limiting their generalizability across diverse manipulation scenarios. We present DexSim2Real, an integrated framework that leverages vision-language foundation models to bridge the sim-to-real gap for dexterous manipulation. Our system combines three components: (1) Foundation Model-Guided Domain Randomization (FM-DR), which uses a vision-language model as a visual realism critic to optimize simulation parameters via closed-loop CMA-ES, complementing text-based approaches like DrEureka with direct visual feedback; (2) a Tactile-Visual Cross-Attention Policy (TVCAP) that adapts cross-attention visuo-tactile fusion to zero-shot sim-to-real RL; and (3) a Progressive Skill Curriculum (PSC) that builds on LLM-based task decomposition with a difficulty scheduler tailored to contact-rich dexterous tasks. Extensive experiments on six challenging manipulation tasks with blinded evaluation demonstrate that DexSim2Real achieves a 78.2% average real-world success rate, outperforming DrEureka and DeXtreme while reducing the sim-to-real performance gap to only 8.3%.
Comments: 13 pages, 2 figures, 5 tables
Subjects:
Robotics (cs.RO); Machine Learning (cs.LG)
Cite as: arXiv:2605.05241 [cs.RO]
(or arXiv:2605.05241v1 [cs.RO] for this version)
https://doi.org/10.48550/arXiv.2605.05241
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From: Zijian Zeng [view email] [v1] Sun, 3 May 2026 17:29:29 UTC (37 KB)
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