SkillVLA: 通过技能复用解决双臂操作中的组合多样性问题
SkillVLA: Tackling Combinatorial Diversity in Dual-Arm Manipulation via Skill Reuse
AI总结：
SkillVLA通过技能复用解决双臂操作中的组合多样性问题，提升技能组合成功率至51%并在复杂任务中表现优异。
摘要：
近期视觉-语言-动作（VLA）模型的进步已显示出在双臂操作中的强大潜力，能够实现复杂行为并在未见环境中实现泛化。然而，主流双臂VLA模型大多忽视了组合多样性这一关键挑战。不同单臂行为的配对可以导致质上不同的任务行为，但现有模型并未显式考虑这种结构。我们认为有效的双臂VLA应支持技能复用——即能够在新的左右配对中重新组合之前学习的单臂技能——从而避免需要分别学习每种可能的组合。当前VLA设计将技能跨臂纠缠，阻止了这种重组并限制了可扩展性。为了解决这一限制，我们提出了SkillVLA，一个专门设计用于双臂操作中实现技能复用的框架。大量实验表明，SkillVLA显著提高了技能组合，使总体成功率从0%提升到51%，并在协作和长周期任务中实现了强劲表现。
英文摘要：
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.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
