用镜像演示对提升机器人学习效率，少数据也能跨空间迁移。
MirrorDuo: Reflection-Consistent Visuomotor Learning from Mirrored Demonstration Pairs
- 通过镜像生成互补演示，实现‘一学双用’的数据增强。
- 相同数据量下，镜像分布时性能显著提升，零样本可迁移至对称空间。
- 适用于行为克隆与扩散策略，尤其适合少样本跨环境学习场景。
基于图像的行为克隆依赖于由常见RGB摄像头捕获的示范数据。然而，其泛化能力受限于多样示范的收集成本，尤其是在工作空间变化下的表现。我们提出MirrorDuo，一种基于镜像的范式，作用于图像、本体感知及完整的6-DoF末端执行器动作元组，为每条原始示范生成对应的镜像副本，实现‘收集一个，免费获得一个’的效果。该方法可作为现有学习流程（如标准行为克隆或扩散策略）的数据增强策略，也可作为反射等变策略网络的结构先验。通过利用原始与镜像域之间的重叠性，当示范在工作区两侧均匀分布时，相同数据预算下性能显著提升；当示范仅限一侧时，只需0或5个目标侧示范即可高效实现技能迁移。
原文摘要 · Abstract (English)
Image-based behaviour cloning leverages demonstrations captured from ubiquitous RGB cameras. However, it remains constrained by the cost of collecting diverse demos, especially for generalizing across workspace variations. We propose MirrorDuo, a reflection-based formulation that operates on image, proprioception, and full 6-DoF end-effector action tuples, generating a mirrored counterpart for each original demonstration, effectively achieving "collect one, get one for free". It can be applied as a data augmentation strategy for existing learning pipelines, such as standard behaviour cloning or diffusion policy, or as a structural prior for reflection-equivariant policy networks. By leveraging the overlap between the original and mirrored domains, MirrorDuo achieves significantly improved performance under the same data budget when demonstrations are evenly distributed across both sides of the workspace. When demonstrations are confined to one side, MirrorDuo enables efficient skill transfer to the mirrored workspace with as few as zero or five demos in the target arrangement.
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