RoCoDA：面向数据高效的机器人示教学习的反事实数据增强方法
RoCoDA: Counterfactual Data Augmentation for Data-Efficient Robot Learning from Demonstrations
AI总结：
针对机器人模仿学习泛化难、数据成本高的问题，提出融合因果不变性与SE(3)等变性的RoCoDA反事实数据增强框架，在五项操作任务上实现了优于现有方法的策略性能、泛化性与样本效率。
摘要：
机器人领域的模仿学习因机器人环境的复杂性和数据采集的高成本，在泛化性方面面临重大挑战。我们提出RoCoDA这一全新方法，将不变性、等变性和因果性的概念统一在单一框架内，以强化模仿学习的数据增强效果。RoCoDA通过修改环境状态中与任务无关的子集且不影响策略输出，利用因果不变性。同时，我们利用SE(3)等变性，对物体位姿施加刚体变换并调整对应动作，生成合成示教数据。我们在五项机器人操作任务上开展大量实验验证RoCoDA，结果表明，与当前最优的数据增强方法相比，该方法在策略性能、泛化性和样本效率上均有提升。我们的策略对未见过的物体位姿、纹理以及干扰物存在的场景均表现出强泛化性。此外，我们还观察到重新抓取等涌现行为，表明经RoCoDA训练的策略对任务动力学具备更深层的理解。通过结合不变性、等变性和因果性，RoCoDA为模仿学习中的数据增强提供了一种有理论依据的方法，填补了几何对称性与因果推理之间的空白。项目页面：https://rocoda.github.io
英文摘要：
Imitation learning in robotics faces significant challenges in generalization due to the complexity of robotic environments and the high cost of data collection. We introduce RoCoDA, a novel method that unifies the concepts of invariance, equivariance, and causality within a single framework to enhance data augmentation for imitation learning. RoCoDA leverages causal invariance by modifying task-irrelevant subsets of the environment state without affecting the policy's output. Simultaneously, we exploit SE(3) equivariance by applying rigid body transformations to object poses and adjusting corresponding actions to generate synthetic demonstrations. We validate RoCoDA through extensive experiments on five robotic manipulation tasks, demonstrating improvements in policy performance, generalization, and sample efficiency compared to state-of-the-art data augmentation methods. Our policies exhibit robust generalization to unseen object poses, textures, and the presence of distractors. Furthermore, we observe emergent behavior such as re-grasping, indicating policies trained with RoCoDA possess a deeper understanding of task dynamics. By leveraging invariance, equivariance, and causality, RoCoDA provides a principled approach to data augmentation in imitation learning, bridging the gap between geometric symmetries and causal reasoning. Project Page: https://rocoda.github.io
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。
