单次现实演示合成用于可扩展的双臂操作
One-Shot Real-World Demonstration Synthesis for Scalable Bimanual Manipulation
AI总结：
本文提出BiDemoSyn框架，通过单个现实示例生成大量高质量双臂操作演示，结合任务分解与视觉引导对齐，实现高效且物理可行的演示合成，提升机器人操作的泛化能力与数据效率。
摘要：
学习灵活的双臂操作策略严重依赖大规模高质量演示，但现有方法面临固有权衡：遥控提供物理基础数据但劳动强度大，而基于模拟的合成效率高但存在仿真到现实差距。本文提出BiDemoSyn框架，通过分解任务为不变协调块和变量对象依赖调整，结合视觉引导对齐和轻量轨迹优化，生成数千种多样且可行的演示。在六个双臂任务中，训练于BiDemoSyn数据的策略在新物体姿态和形状上表现出强泛化能力，优于现有基线。BiDemoSyn还扩展到少样本合成，提升物体多样性与分布外泛化，同时保持强数据效率。此外，训练于BiDemoSyn数据的策略在新机器人平台上实现零样本跨体态迁移，得益于以物体为中心的观察和简化6自由度末端执行器动作表示，解耦策略与体态特定动力学。通过弥合效率与现实保真度之间的差距，BiDemoSyn提供了一条可扩展的路径，实现复杂双臂操作的实用模仿学习，不牺牲物理基础。
英文摘要：
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.
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。
