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arXiv · 2603.03836

SkillVLA: Tackling Combinatorial Diversity in Dual-Arm Manipulation via Skill Reuse

Xuanran Zhai·Zekai Huang·Longyan Wu·Qianyou Zhao·Qiaojun Yu·Jieji Ren·Ce Hao·Harold Soh
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Abstract

Recent progress in vision-language-action (VLA) models has demonstrated strong potential for dual-arm manipulation, enabling complex behaviors and generalization to unseen environments. However, mainstream bimanual VLA formulations largely overlook the critical challenge of combinatorial diversity. Different pairings of single-arm behaviors can induce qualitatively distinct task behaviors, yet existing models do not explicitly account for this structure. We argue that effective bimanual VLAs should support skill reuse - the ability to recombine previously learned single-arm skills across novel left-right pairings - thereby avoiding the need to separately learn every possible combination. Current VLA designs entangle skills across arms, preventing such recomposition and limiting scalability. To address this limitation, we propose SkillVLA, a framework explicitly designed to enable skill reuse in dual-arm manipulation. Extensive experiments demonstrate that SkillVLA substantially improves skill composition, increasing overall success rate from 0% to 51%, and achieves strong performance on cooperative and long-horizon tasks.

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Xuanran Zhai, Zekai Huang, Longyan Wu, Qianyou Zhao, Qiaojun Yu, Jieji Ren, Ce Hao, Harold Soh. 2026-03-04. SkillVLA: Tackling Combinatorial Diversity in Dual-Arm Manipulation via Skill Reuse. https://arxiv.org/abs/2603.03836

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