MoDex: Towards Universal Multi-Object Dexterous Grasping

Abstract:Multi-object grasping is a fundamental yet challenging problem, constrained by data collection costs, varying spatial layouts, and the need for broad generalization. To address these challenges, we propose MoDex, a data-efficient framework that provides the first unified solution for universal multi-object dexterous grasping across diverse spatial layouts. MoDex accomplishes this by formulating the task as a single-step decision problem of learning residual corrections to a reference trajectory, and introducing a spatially adaptive dual-strategy that performs single-shot grasping for proximal objects and sequential interaction for separated objects. Extensive evaluations show that MoDex significantly outperforms baselines in proximal configurations, achieving 61.24% success on unseen objects, and achieves high success rates in separated layouts. Further experiments demonstrate its generalization across robot embodiments, scalability and zero-shot sim-to-real deployment through a vision-based policy trained solely on simulation data. Code and pretrained models are available at this https URL.

Accepted at CoRL 2026. Code: this https URL

Robotics (cs.RO)

arXiv:2610.23888 [cs.RO]

(or arXiv:2610.23888v1 [cs.RO] for this version)

https://doi.org/10.48550/arXiv.2610.23888 Focus to learn more arXiv-issued DOI via DataCite

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