Achieving generalizable bimanual manipulation requires systems that can learn efficiently from minimal human input while adapting to real-world uncertainties and diverse embodiments. Existing approaches face a dilemma: imitation policy learning demands extensive demonstrations to cover task variations, while modular methods often lack flexibility in dynamic scenes. We introduce VLBiMan, a framework that derives reusable skills from a single human example through task-aware decomposition, preserving invariant primitives as anchors while dynamically adapting adjustable components via vision-language grounding. This adaptation mechanism resolves scene ambiguities caused by background changes, object repositioning, or visual clutter without policy retraining, leveraging semantic parsing and geometric feasibility constraints. Moreover, the system inherits human-like hybrid control capabilities, enabling mixed synchronous and asynchronous use of both arms. Extensive experiments validate VLBiMan across tool-use and multi-object tasks, demonstrating: (1) a drastic reduction in demonstration requirements compared to imitation baselines, (2) compositional generalization through atomic skill splicing for long-horizon tasks, (3) robustness to novel but semantically similar objects and external disturbances, and (4) strong cross-embodiment transfer, showing that skills learned from human demonstrations can be instantiated on different robotic platforms without retraining. By bridging human priors with vision-language anchored adaptation, our work takes a step toward practical and versatile dual-arm manipulation in unstructured settings.

翻译：实现可泛化的双臂操作需要系统能够从最小的人类输入中高效学习，同时适应现实世界的不确定性和多样化具身形态。现有方法面临两难困境：模仿策略学习需要大量示范以覆盖任务变体，而模块化方法在动态场景中往往缺乏灵活性。我们提出VLBiMan框架，该框架通过任务感知分解从单一人类示例中提取可复用技能，保留不变性基元作为锚点，同时通过视觉-语言接地动态调整可自适应组件。该自适应机制利用语义解析和几何可行性约束，无需策略重新训练即可解决由背景变化、物体重定位或视觉杂乱引起的场景歧义。此外，系统继承了类人混合控制能力，支持双臂的同步与异步混合操作。大量实验验证了VLBiMan在工具使用和多物体任务中的表现，证明了：(1) 与模仿基线相比示范需求的大幅降低；(2) 通过原子技能拼接实现长时域任务的组合泛化；(3) 对新颖但语义相似的物体和外部干扰的鲁棒性；(4) 强跨具身迁移能力，即从人类示范习得的技能可在不同机器人平台上无需重新训练即可实例化。通过桥接人类先验与视觉-语言锚定自适应，本工作向非结构化环境中实用且通用的双臂操作迈进一步。