D-CODA：用于协调双臂数据增强的扩散方法
D-CODA: Diffusion for Coordinated Dual-Arm Data Augmentation
AI总结：
本文提出D-CODA，利用扩散模型为眼在手上双手模仿学习生成视角一致的双臂腕部图像及关节动作标签，并通过约束优化保证接触约束，在仿真与真实任务中验证了其优于基线方法。
摘要：
学习双手操作具有挑战性，因为其维度高且两臂之间需要紧密协调。使用腕部摄像头的眼在手上模仿学习通过聚焦于任务相关视角简化了感知。然而，收集多样化的演示仍然成本高昂，这激发了对可扩展数据增强的需求。尽管先前工作已在单臂场景中探索了视觉增强，但将这些方法扩展到双手操作需要生成两臂间视角一致的观测，并产生既有效又可行的相应动作标签。在这项工作中，我们提出了用于协调双臂数据增强的扩散方法（D-CODA），这是一种针对眼在手上双手模仿学习定制的离线数据增强方法，训练扩散模型为两臂合成新颖且视角一致的腕部摄像头图像，同时生成关节空间动作标签。它采用约束优化来确保涉及夹爪与物体接触的增强状态遵守适合双手协调的约束。我们在5个仿真任务和3个真实世界任务上评估了D-CODA。我们在2250次仿真试验和300次真实世界试验中的结果表明，它优于基线和消融方法，展示了其在眼在手上双手操作中可扩展数据增强的潜力。我们的项目网站位于：https://dcodaaug.github.io/D-CODA/。
英文摘要：
Learning bimanual manipulation is challenging due to its high dimensionality and tight coordination required between two arms. Eye-in-hand imitation learning, which uses wrist-mounted cameras, simplifies perception by focusing on task-relevant views. However, collecting diverse demonstrations remains costly, motivating the need for scalable data augmentation. While prior work has explored visual augmentation in single-arm settings, extending these approaches to bimanual manipulation requires generating viewpoint-consistent observations across both arms and producing corresponding action labels that are both valid and feasible. In this work, we propose Diffusion for COordinated Dual-arm Data Augmentation (D-CODA), a method for offline data augmentation tailored to eye-in-hand bimanual imitation learning that trains a diffusion model to synthesize novel, viewpoint-consistent wrist-camera images for both arms while simultaneously generating joint-space action labels. It employs constrained optimization to ensure that augmented states involving gripper-to-object contacts adhere to constraints suitable for bimanual coordination. We evaluate D-CODA on 5 simulated and 3 real-world tasks. Our results across 2250 simulation trials and 300 real-world trials demonstrate that it outperforms baselines and ablations, showing its potential for scalable data augmentation in eye-in-hand bimanual manipulation. Our project website is at: https://dcodaaug.github.io/D-CODA/.
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。
