Learning dexterous bimanual manipulation policies critically depends on large-scale, high-quality demonstrations, yet current paradigms face inherent trade-offs: teleoperation provides physically grounded data but is prohibitively labor-intensive, while simulation-based synthesis scales efficiently but suffers from sim-to-real gaps. We present BiDemoSyn, a framework that synthesizes contact-rich, physically feasible bimanual demonstrations from a single real-world example. The key idea is to decompose tasks into invariant coordination blocks and variable, object-dependent adjustments, then adapt them through vision-guided alignment and lightweight trajectory optimization. This enables the generation of thousands of diverse and feasible demonstrations within several hours, without repeated teleoperation or reliance on imperfect simulation. Across six dual-arm tasks, we show that policies trained on BiDemoSyn data generalize robustly to novel object poses and shapes, significantly outperforming recent strong baselines. Beyond the one-shot setting, BiDemoSyn naturally extends to few-shot-based synthesis, improving object-level diversity and out-of-distribution generalization while maintaining strong data efficiency. Moreover, policies trained on BiDemoSyn data exhibit zero-shot cross-embodiment transfer to new robotic platforms, enabled by object-centric observations and a simplified 6-DoF end-effector action representation that decouples policies from embodiment-specific dynamics. By bridging the gap between efficiency and real-world fidelity, BiDemoSyn provides a scalable path toward practical imitation learning for complex bimanual manipulation without compromising physical grounding.

翻译：学习灵巧的双臂操作策略高度依赖于大规模、高质量的示范数据，然而当前范式面临固有矛盾：遥操作虽能提供物理上真实可靠的数据，但人力成本过高；基于仿真的数据合成虽效率高，却受限于仿真到现实的迁移落差。我们提出BiDemoSyn框架，该框架能从单个真实世界示范中合成具有物理可行性且富含接触信息的双臂操作示范。其核心思想是将任务分解为不变的协调模块与可变的物体相关调整，再通过视觉引导对齐与轻量级轨迹优化实现适配。该方法可在数小时内生成数千个多样化且可行的示范数据，无需重复遥操作或依赖不完美的仿真环境。在六项双臂任务中，基于BiDemoSyn数据训练的策略能稳健泛化至全新的物体位姿与形状，显著超越近期强基线模型。除单次示范场景外，BiDemoSyn还能自然扩展至少样本合成范式，在保持高数据效率的同时提升物体多样性及分布外泛化能力。更重要的是，基于BiDemoSyn数据训练的策略展现出零样本跨实体迁移能力——通过采用以物体为中心的观测表达与简化的六自由度末端执行器动作表征，策略与特定实体动力学实现解耦。通过弥合效率与真实世界保真度之间的鸿沟，BiDemoSyn为复杂双臂操作的实用化模仿学习开辟了一条不牺牲物理根基的可扩展路径。